sample survey research papers

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Doing Survey Research | A Step-by-Step Guide & Examples

Doing Survey Research | A Step-by-Step Guide & Examples

Published on 6 May 2022 by Shona McCombes . Revised on 10 October 2022.

Survey research means collecting information about a group of people by asking them questions and analysing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyse the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyse the survey results, step 6: write up the survey results, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research: Investigating the experiences and characteristics of different social groups
• Market research: Finding out what customers think about products, services, and companies
• Health research: Collecting data from patients about symptoms and treatments
• Politics: Measuring public opinion about parties and policies
• Psychology: Researching personality traits, preferences, and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and longitudinal studies , where you survey the same sample several times over an extended period.

Prevent plagiarism, run a free check.

Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• University students in the UK
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18 to 24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalised to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every university student in the UK. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalise to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions.

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by post, online, or in person, and respondents fill it out themselves
• An interview , where the researcher asks a set of questions by phone or in person and records the responses

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by post is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g., residents of a specific region).
• The response rate is often low.

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyse.
• The anonymity and accessibility of online surveys mean you have less control over who responds.

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping centre or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g., the opinions of a shop’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations.

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data : the researcher records each response as a category or rating and statistically analyses the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analysed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g., yes/no or agree/disagree )
• A scale (e.g., a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g., age categories)
• A list of options with multiple answers possible (e.g., leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analysed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an ‘other’ field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic.

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by post, online, or in person.

There are many methods of analysing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also cleanse the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organising them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analysing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analysed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyse it. In the results section, you summarise the key results from your analysis.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, October 10). Doing Survey Research | A Step-by-Step Guide & Examples. Scribbr. Retrieved 3 June 2024, from https://www.scribbr.co.uk/research-methods/surveys/

Is this article helpful?

Shona McCombes

Other students also liked, qualitative vs quantitative research | examples & methods, construct validity | definition, types, & examples, what is a likert scale | guide & examples.

• Have your assignments done by seasoned writers. 24/7
• Contact us:
• +1 (213) 221-0069
• [email protected]

How to Write a Survey Paper: A stepwise Guide with Examples

How to Write a Survey Paper

Some of you may be wondering what a survey paper is. A survey paper contains the interpretation that has been drawn by the author after they have reviewed and analyzed various research papers that are centered on a specific topic. Those research papers should be already published.

Now that we have understood what a survey paper is, let us explore the various steps that have to be taken when coming up with a survey paper. As noted, a survey paper lists and analyzes the most recent research work in a particular area of study.

To write a good survey paper, you need to research the representative papers, come up with a title, a good abstract, and writing the introduction, the body, and conclusions that reflect the findings as well as the challenges of the study.

To do this, there is a challenge of research. As such, the first challenge is to find the most recent and appropriate research papers for the topic. The 9 steps below should be followed when writing a survey paper.

Need Help with your Homework or Essays?

Step 1: selecting the representative papers.

The first step when writing a survey paper is selecting the most relevant representative papers that are within the scope of your research and summarizing them effectively. As you will note, there can be a lot of research papers, and the space required to create a survey paper is limited.

During such, it can be challenging when trying to pick the key work within the scope of your study.

As an author of the survey paper, you will have to read the research papers’ abstracts and conclusions and pick the subset that captures your area of study.

To ensure that the selected research papers are appropriate or relevant, they should be recent, contain more citations, and be published in journals with a high reputation.

The research papers should not be less than 10.

Step 2: Coming up with an Appropriate Title

The second step is coming up with a captivating title that provides a clear summary of your paper’s contents. As such, the title should be clear and brief. To achieve this, the title should utilize active verbs rather than complex phrases that are based on nouns. 

A good title of your survey paper should contain between 10 and 12 words because a title with more words will divert the attention of the readers from the central point.

A longer title will also appear unfocused. Therefore, the title should have the keywords of your survey paper in such a way that it defines the study’s nature. 

Step 3: Creating an Abstract

Another important step to be taken when writing a survey paper is to create an abstract. The abstract acts as a summary of your survey paper.

It should provide a summary of the problem that has been investigated, the methods used, the results of the study, and the conclusion.

Abstracts summarize the most important contents of your survey paper in a single paragraph of between 200 and 300 words.

When creating an abstract, make sure that it contains or highlights the key points while convincing the readers or the target audience to continue reading the whole survey paper. Should always include an abstract in your survey paper.

Step 4: Listing Key Terms

While the keywords help the target audience or other researchers understand the field of the survey paper, the subfield, research issue, the topic, and so on, the main purpose of this section is to help readers or researchers locate your paper when they are doing searches on the topic.

Most of the databases, electronic search engines such as Google, and journal websites will utilize keywords when deciding whether to display the survey paper to interested readers and when this should be done.

With the proper keywords, your survey paper will be more searchable and it will be cited by more researchers because it can be easily located. 

Step 5: Writing the Introduction

The next step when writing a survey paper is to include a good introduction.

A good introduction paragraph will explain to the target readers how the research problem has been tackled by the research papers that you have included in your paper.

The introduction should arouse the readers’ interest in knowing more about the topic and the research domain. If they are interested, they will continue reading your survey paper.

Unlike the abstract, the introduction within a survey paper does not contain a very strict word limit. However, it should be concise because it introduces the paper’s topic, provides a broader context of the study, and gradually narrows the scope down to the research problem. 

Therefore, make sure that your introduction sets a scene and contextualizes your paper. It can begin with a historical narrative bringing the narrative to the present day and ending with a research question. Ensure that the very last sentence of your introduction is the thesis statement. 

Step 6: Providing the Approaches Used in the Survey Paper

This is a very important step in any survey paper. This is where you are required to provide the methodologies used to conduct your research or survey in a logical order.

You are required to logically move from one method to the next as you clearly define each approach at the beginning of every section.

To ensure that your readers are at par with you, you should share the motivation behind each methodology. This is achieved by giving a high-level summary of every approach and then narrowing it down to the specific approaches.

You should also demonstrate the applicability and the practicability of every approach used in the research, and the areas that need to be improved. You should graphically visualize at least one method used. 

Step 7: Writing About the Paper Surveys

This step should take the bulk of your survey paper because it is the point where you survey the papers you have selected. Here, you should decide what you are going to inform your readers about each research paper.

Therefore, it is important to first read the research papers in a manner that you can know what to inform your readers about them.

For each research paper, make sure that you tell your readers about their research direction. Also, ensure that you identify the algorithms or mathematical techniques the research papers rely on and whether they are application or theory papers. 

You should also state whether the selected research papers are an improvement on other works or they are a continuation of other works.

Then, state whether the research papers utilize simulations, theoretical proofs, real-life deployment, and so on. Finally, you should state the strengths and weaknesses of each research paper, authors’ claims, and assumptions. 

Step 8: Research Challenges

After surveying every research paper you have utilized, the next step is to state the challenges you encountered while conducting research.

When writing a survey paper, you will always face various challenges.

Such challenges can be finding the best or most appropriate research papers, comparing them to determine their strengths, and so on.

Other challenges can arise from the research papers themselves. This can include their delivery of results. Some research papers will contain confusing data. 

Step 9: Coming up with a Conclusion

Finally, the conclusion should answer the questions that have been raised by your survey paper’s objectives and goals.

Though it should be interesting and captivating, it should still be presented academically. It should be objective and offer a final say concerning the survey’s subject. 

The conclusion should synthesize the results by proving their interpretation, propose the course of action as per the results, and offer solutions to the issues that have been identified.

The reader should be capable of understanding the whole survey paper by reading the conclusion. Therefore, ensure that your conclusion synthesizes your paper. 

Get a Brilliant Essay today!

Let our essay writing experts help you get that A in your next essay. Place your order today, and you will enjoy the benefits.

Tips When Writing a Good Survey Paper

The first tip in writing a good survey paper is to select the most appropriate and latest research papers that will be used in the paper. This is a very important tip because the survey paper will be completely based on them. Old research papers will render your survey paper useless.

Research papers that are not within the scope of your research or topic will also render the survey paper useless.

The second tip is to make sure that you come up with a concise topic that will summarize what your paper is about.

It is also very important to follow the appropriate format of a survey paper.

The format, after you have written your title, should be abstract, key terms, introduction, approaches or methodologies, conducting surveys for every paper used, research challenges, and finally the conclusion.

Another important tip is to utilize more than 10 research papers for the survey. Then can be even more than 20 depending on the scope of your study. The more the research papers used in your survey paper, the more professional and credible it will appear. 

It should be noted that a good survey paper will utilize research papers that are recent (not more than 5 years) and have more academic sources.

To increase the credibility of your survey paper, the research papers used should come from reputable journal sources or publications. In our guide to writing good research papers , we explained more about references. Check it out.

Also, note that the process of writing a survey paper is much different from that of writing an issue paper or doing opinion essays . Therefore, each step needs to relate to the survey.

15 Examples of Topics for Writing a Survey Paper

• Advances in leaf image analysis for bacterial disease detection
• A survey on the impact of social media among youths in the united states
• A Survey on leaf image analysis for bacterial disease detection
• Recent trends in the electric cars manufacturing industry
• Recent trends in perinatal care: Exploring the major causes of perinatal mortality
• Leaf image analysis for bacterial disease detection
• Advances in curriculum-based education: A survey on educational trends in sub-Saharan Africa
• Recent trends in environmental awareness campaigns in low-income countries
• A survey on COVID-19 pandemic impact on the united states economy
• Recent trends in the immunization approach taken by third world countries after the second and third wave of COVID-19 disease
• Advances in semiconductor manufacturing for BMW electronic cars
• A survey on the impact of 5-G connectivity among SMEs in Britain
• Recent trends in the space race: A survey of how the founders of Virgin Atlantic, Tesla, and Amazon are competing to dominate space travel 
• Advances in care for pressure ulcers: A survey on the impact of frequent automated turning on older immobile patients in Germany
• A survey on the impact of geopolitics on peace within the Middle East 

Josh Jasen or JJ as we fondly call him, is a senior academic editor at Grade Bees in charge of the writing department. When not managing complex essays and academic writing tasks, Josh is busy advising students on how to pass assignments. In his spare time, he loves playing football or walking with his dog around the park.

Related posts

essay writing problems

10 Essay Writing Problems and their Easy Solutions

writing essay on your phone

How to Write an Essay or paper on your Phone: 3 Easy Ways

essay writing is important

7 reasons why Writing is Important in College & in Life

Research Voyage

Research Tips and Infromation

How to Write a Better Survey Paper in 06 Easy Steps?

Welcome to this comprehensive guide on crafting a better survey paper. Survey papers play a crucial role in summarizing and presenting state-of-the-art knowledge within a specific domain. Whether you are a seasoned researcher looking to refine your survey writing skills or a budding academic endeavouring to embark on your first survey paper journey, these six fundamental steps will serve as your compass in navigating the survey paper writing process.

Introduction

A Researcher begins his research journey by first writing a survey paper in the domain of his research. Writing a survey paper helps a researcher to

i) Understand his domain of research thoroughly

ii) Identify the existing research gaps,

iii) Understand the various parameters and their role in solving the problem and

iv) Infrastructure and Data set requirements for research.

In fact, after completing my survey paper I decided to go for optimal cloud infrastructure for my work. This helped me a lot in cutting the total cost of my research.

A survey paper is also a service to the scientific community. You are doing research for young research scholars. Instead of reading a vast amount of papers to understand what a scientific topic is about,  a researcher just needs to read your paper and can start his research at the earliest with a clear direction in mind.  To make the researcher read your paper, you must have good content and a high citation score . Otherwise, your paper is like purchasing a site in the forest and trying to sell with no one ready to buy.

What is expected in a survey paper? A survey paper is a research paper which lists and analyses the latest research works in a particular research domain of interest. The survey paper derives some conclusions from the work carried out so far and provides new avenues for future research.

A good survey paper provides a concise but broad review of a domain that is accessible to a wide range of readers who are naive and willing to carry out research in the domain presented. This introduces two primary challenges for writing such a  survey paper.

The first challenge is to pick representative papers from within the research area and summarize them. There can be a vast amount of research papers available and survey paper has limited space to capture the critical work in the field.

The author needs to go through abstracts and conclusions for a relatively large number of papers and select a subset that covers the selected topic area for detailed reading and presentation in the survey. Identifying the papers having higher citations and which are published in conferences and journals of high reputation will have to be given higher priority for selection.

The second challenge is to make the reader comfortable in reading and comprehending the analysis done for the various research papers. The author has to go through each paper considered for the survey at least two-three times before deriving any conclusion.

How to Make a Survey Paper?

A survey paper should

• Pick at least  10-20  papers on a specific topic from the collected paper list.
• The papers selected should be a mix of papers including the base paper in the selected domain to the most recently published paper.
• Should have an analysis of the significance of the approach and the results presented in each paper
• Give a critical assessment of the work that has been done.
• Include a discussion on future research directions
• Give precise  details of the experimental setup used for carrying out research in each paper
• Compare only those works which have a common experimental platform or data set. Otherwise, you have to recreate a common platform or use a common data set and test the methodologies used in various platforms.

A typical structure of a  survey paper includes the following 06 Sections as discussed below:

Survey Paper Format

[Title of Your Survey Paper]

2. Abstract:

[Summarize the purpose, methodology, key findings, and implications of your survey paper in a concise paragraph.]

3. Key Terms:

[Provide a list of key terms or concepts relevant to your survey paper.]

4. Introduction:

[Provide an overview of the topic and its importance. Describe the scope and objectives of your survey paper. Briefly introduce the main themes and topics covered in the literature review.]

5. Literature Review:

I. complexity of the problem: static vs dynamic.

• [Brief overview of the complexity of the problem, including both static and dynamic aspects.]
• [Discussion of static aspects of the problem, including definitions, models, algorithms, etc.]
• [Discussion of dynamic aspects of the problem, including evolving nature, real-time constraints, etc.]

II. Design Space

• [Brief overview of the design space related to the problem.]
• [Discussion of various design considerations and factors influencing the problem-solving approach.]
• [Overview of different approaches and solutions proposed in the literature for addressing various aspects of the problem.]

6. Conclusion:

[Summarize the main findings and insights gained from the literature review. Highlight any gaps or limitations in current research. Discuss potential future directions for research in the field.]

The Six Steps for Writing a Better Survey Paper

In this comprehensive guide on how to write a better survey paper, we will explore six fundamental steps that will elevate the quality and impact of your research.

It all begins with crafting an eye-catching “Title” that succinctly conveys the essence of your survey paper and captures readers’ attention.

Moving on, the “Abstract” section serves as a concise overview of your research, providing readers with essential insights into your objectives, methodology, and findings.

Identifying the key terms of your literature review is the core of your survey paper, where you analyze existing research. Identify and emphasize key terms and concepts to provide a clear understanding of the relevant research landscape.

As you progress, a strong “Introduction” sets the tone, introducing the problem, its significance, and the objectives of your survey paper.

The heart of your survey paper lies in the “Literature Review,” where you analyze existing research. By highlighting key terms and concepts, you enhance clarity and enable readers to grasp the current research landscape more effectively.

As your survey paper reaches its “Conclusion,” you synthesize the key findings from the literature review and offer valuable insights into the current state of the field.

Lastly, by thoroughly reviewing, revising, and refining your survey paper, you ensure clarity, coherence, and overall excellence.

By following these six steps, your survey paper will not only make a significant contribution to your chosen field but also captivate and inform your readers with its well-structured and insightful content.

1. Eye-Catching “Title” for the Survey Paper

The primary function of a title is to provide a clear summary of the paper’s content. So keep the title brief and clear. Use active verbs instead of complex noun-based phrases, and avoid unnecessary details.   Moreover, a good title for a research paper is typically around 10 to 12 words long. A lengthy title may seem unfocused and take the readers’ attention away from an important point.  As I supervise many candidates who hail from non-native English-speaking countries, they struggle a lot with writing error-free Titles. So, I always advise them to take English classes in parallel with their research.

A good research paper title should contain keywords used in the manuscript and should define the nature of the study. Think about terms people would use to search for your study and include them in your title.  Do not use abbreviations in the title.  Knowing the search intent of the people who search for the keyword is critical as it helps you to find the top searched keywords. I learnt this technique by mapping SEO-based keyword research techniques to find quality keywords for my title.

Usually, a Title for a survey paper starts with ,   “Recent trends in ….”, “Advances in…..”.

Some survey papers end with “…….: A Survey” .

Here are the other ways to mention survey paper titles:

• ” A Survey on …..”
• “…….: A Survey”
• “An Overview of…”
• “A Comprehensive Study on…”
• “Exploring the Landscape of…”
• “A Critical Review of…”
• “A Systematic Analysis of…”
• “Examining the State of…”
• “A Comprehensive Review on…”
• “Surveying the Current Trends in…”
• “Investigating the Advancements of…”
• “Analyzing the Evolution of…”

Here is a list of Examples of Survey Paper Titles

• “A Survey on Artificial Intelligence Applications in Healthcare”
• “Recent Trends in Renewable Energy Sources: A Survey”
• “Advances in Natural Language Processing Techniques: A Survey”
• “A Survey on Cybersecurity Threats and Mitigation Strategies”
• “Recent Developments in Blockchain Technology: A Survey”
• “Advancements in Machine Learning Algorithms for Image Recognition: A Survey”
• “A Survey on Cloud Computing Security and Privacy Issues”
• “Recent Trends in E-commerce Payment Systems: A Survey”
• “Advances in Robotics and Automation: A Survey”
• “Mobile Health Applications: A Survey of Current Trends and Challenges”
• “An Overview of Machine Learning Algorithms: A Comprehensive Study”
• “A Critical Review of Renewable Energy Technologies: Exploring the Landscape”
• “Examining the State of Cybersecurity Threats: A Systematic Analysis”
• “A Comprehensive Study on Blockchain Technology: Investigating the Advancements”
• “Analyzing the Evolution of Artificial Intelligence Applications: A Survey”
• “A Survey of Cloud Computing Security and Privacy Issues”
• “Recent Trends in Natural Language Processing Techniques: A Comprehensive Review”
• “Surveying the Current Trends in E-commerce Payment Systems”
• “Exploring the Landscape of Robotics and Automation: A Critical Review”
• “A Comprehensive Review on Mobile Health Applications: Examining the State of the Art”

For more details on writing Titles for your research paper visit my blog post on: Research Paper Title: 03 Simple Steps to Make it Easily Discoverable

2. Giving an Overview of the Survey Paper Through the “Abstract” Section

The abstract is a summary of a research paper describing the problem investigated, the methods applied, the main results and conclusions. Abstracts are a good way to summarise the key contents of a paper, from the research it uses to the ideas you want to share with the reader.

The abstract is a single paragraph containing a minimum of 200 words up to 300 words. An abstract offers a preview that highlights key points and helps the audience decide whether to view the entire work.

Extraction of meaningful leaf disease features by applying image processing techniques is a problem that has been studied by the image processing community for decades.  Image processing research for leaf disease identification has matured significantly throughout the years and many advances in image processing techniques continue to be made, allowing new techniques to be applied to new and more demanding pathological problems. In this paper, we review recent advances in diseased part extraction of leaf images affected by pathogens, focusing primarily on three important Soft computing techniques namely: Neural networks, Fuzzy logic and  Genetic algorithms. Throughout, we present tables that summarize and draw distinctions among key ideas and approaches. Where available, we provide comparative analyses, and we make suggestions for analyses yet to be done.

Here’s a tabular representation of the sub-sections based on the given abstract:

Please note that this tabular representation is a simplified breakdown of the abstract into distinct sub-sections based on its content. The actual structure and headings may vary depending on the specific formatting requirements and guidelines of the paper.

For more details on how to write an Abstract for your research paper, you can visit my blog post on :

• Research Paper Abstract: 10 Simple Steps to Make a Big Difference

3. Highlighting “Key Terms” of the Survey Paper

The purpose of keywords in a research paper is to help other researchers find your paper when they are searching for the topic. Keywords define the field, subfield, topic, research issue, etc. that are covered by the article.

Most electronic search engines, databases, or journal websites use keywords to decide whether and when to display your paper to interested readers. Keywords make your paper searchable and ensure that you get more citations. Thus, it is important to include the most relevant keywords that will help other authors find your paper.

For Example for the abstract written in the previous section, the keywords can be: Keywords: Plant pathology, bacterial blight, diseased part extraction, Image processing, Soft Computing.

For more details on  identifying the most prominent keywords for your research paper, you can visit my blog post on: Top 10 Rules to Identify Keywords for your Research Paper

4. Building a Strong “Introduction” Section of the Survey Paper

A good introduction in a survey paper explains how the research problem has been solved by various researchers and creates ‘leads’ to make the reader want to delve further into the research domain.  Introduce the terminology of the field and describe what the various terms mean.

The introduction does not have a strict word limit, unlike the abstract, but it should be as concise as possible. The introduction works upon the principle of introducing the paper’s topic and setting it into a broad context, gradually narrowing it down to a research problem.

The main task of the introduction is to set the scene, giving your paper a context and seeing how it fits in with previous research in the field. The first few paragraphs of your introduction can be based on a historical narrative, from the very first research in the field to the current day.

The entire introduction should logically end with the research question. The reader, by the end of the introduction, should know exactly what research issue you are trying to survey with your paper.

Here’s a tabular format of the introduction for your survey paper:

Please note that the actual sub-section headings and their order may vary depending on the specific content and focus of your survey paper. The table above provides a general structure based on the information given in the previous response.

Example of an Introduction:

In this survey paper, we aim to explore the advancements made in extracting meaningful leaf disease features through image processing techniques. Over the years, this intricate problem has garnered significant attention from the image-processing community. Our objective is to provide a comprehensive overview of the diverse approaches employed by various researchers to address this challenge successfully.

To set the context, we will introduce the key terminologies used in this research domain and define their significance. As we delve into the topic, we will progressively narrow down the scope, focusing on the core research problem of extracting diseased parts from leaf images.

Throughout the introduction, we will present a historical narrative, tracing the evolution of image processing techniques for leaf disease identification from their inception to the current state-of-the-art methodologies.

By adopting this approach, we aim to give readers a clear and concise understanding of the research landscape in this field. As we progress, we will create ‘leads’ that encourage readers to delve deeper into the intricacies of diseased part extraction.

By the end of this introduction, readers will have a definitive grasp of the central research question we address in this survey paper. We will culminate with a concise statement of the research issue, guiding readers towards an exploration of recent advances in diseased part extraction using prominent Soft computing techniques, specifically Neural networks, Fuzzy logic, and Genetic algorithms.

Example text included under each subsection heading:

For more details on writing the introduction section, you can visit my blog post on: How to Write an Effective Research Paper Introduction in 03 easy steps?

5. The “Literature Review” of the Survey Paper

The Literature Review has to be based on the specific theme of research which will help the reader in focusing his/her research on specific concepts. In this section, the provided details themes serve as a foundation for your Survey Paper. To create a comprehensive survey paper, it is essential to extend each theme with detailed analysis, research gaps, in-depth block diagrams, functioning descriptions, comparative analysis, and other relevant elements. By thoroughly exploring and analyzing the existing literature, you can enrich the survey paper with critical insights, identify research challenges, and provide valuable contributions to the field. Your thorough examination will contribute to a complete and well-rounded survey paper on the chosen topic.

Some possible  themes can be:

i. Complexity of the problem:

There can be various types of solutions for a  given problem domain and the author has to organize them in the increasing level of complexity or scale.

Literature Review: Complexity of Scene Analysis in Image Processing

In the field of Image Processing, scene analysis emerges as a core problem, where researchers seek to extract meaningful information from visual data. The solutions for scene analysis can range from simple grayscale images with few objects against a constant background to complex images with multiple objects of varying shapes and colours against diverse backgrounds.

At the basic level of complexity, researchers have explored methods for segmenting simple grayscale images containing only one or two objects of identical shapes against a uniform background. Early studies in the literature focused on techniques like thresholding and edge detection to identify and distinguish objects. (Reference: Smith et al., 2005; Johnson and Brown, 2008)

Moving up the complexity ladder, the literature presents solutions for scenes with multiple objects of different shapes but with a consistent background. Researchers have proposed methods such as region growing and contour tracing to extract relevant objects from such images. (Reference: Lee and Kim, 2010; Chen et al., 2012)

As the complexity further increases, scene analysis encompasses images with diverse objects of varying shapes and colours set against complex backgrounds. In these cases, advanced algorithms like Convolutional Neural Networks (CNNs) and deep learning techniques have been deployed to achieve accurate and robust object recognition. (Reference: Wang et al., 2016; Zhang and Li, 2018) .

Additionally, research has expanded into real-world scenarios, where scene analysis encounters challenges such as occlusions, illumination variations, and cluttered backgrounds. Addressing these complexities, the literature explores techniques like scale-invariant feature transform (SIFT) and histogram of oriented gradients (HOG) to handle object detection and recognition in challenging environments. (Reference: Liu et al., 2019; Park and Lee, 2020)

The progression of complexity in scene analysis solutions reveals the evolution of Image Processing techniques to accommodate real-world challenges. By organizing the literature review based on the increasing level of complexity, this survey paper aims to assist readers in understanding the advancements made in addressing scene analysis across diverse image scenarios.

ii. Static vs. Dynamic:

Many fields can be organized by static techniques, dynamic techniques, and even hybrid. For example Static or Dynamic Routing in Computer Networking.

Literature Review: Static vs. Dynamic Resource Allocation in Cloud Computing

Resource allocation is a critical aspect of Cloud Computing, ensuring efficient utilization of computational resources to meet user demands and optimize system performance. The literature reveals two prominent approaches to resource allocation in Cloud Computing: Static and Dynamic allocation.

Static Resource Allocation involves allocating resources based on predetermined configurations and user-defined policies. Researchers have proposed various static resource allocation algorithms, such as Round-Robin and First-Come-First-Serve (FCFS), to allocate resources in a fixed manner without considering varying resource demands (Reference: Smith et al., 2015; Johnson and Brown, 2017).

In contrast, Dynamic Resource Allocation refers to adaptive resource allocation that adjusts resources in real-time based on changing workload conditions. Dynamic resource allocation algorithms, such as Elastic Load Balancing and Auto-scaling, continuously monitor resource usage and adjust allocations to optimize performance and maintain service-level agreements (Reference: Lee and Kim, 2018; Chen et al., 2020).

The literature also explores hybrid resource allocation techniques that combine elements of both static and dynamic approaches. Hybrid approaches aim to strike a balance between the predictability of static allocation and the responsiveness of dynamic allocation. For instance, researchers have proposed a hybrid approach that initially uses static allocation for steady-state workloads but switches to dynamic allocation during periods of sudden resource demand spikes (Reference: Wang et al., 2019; Zhang and Li, 2021).

By analyzing the literature on static, dynamic, and hybrid resource allocation techniques in Cloud Computing, this survey paper provides readers with insights into the trade-offs between these approaches. Additionally, it highlights the evolution of resource allocation strategies and their impact on cloud performance, cost efficiency, and scalability.

Please note that the references used in this example are imaginary.

iii. Segregating the Design Space:

Many systems are made up of components, so maybe for a  computer network paper, the author could divide the problems into a physical layer, application layer, session layer, transport layer, data link layer and physical layer.

Literature Survey: Segregating the Design Space in AI-based Recommender Systems

In recent years, Artificial Intelligence (AI) has driven significant advancements in recommender systems, revolutionizing personalized recommendations across various domains. To comprehensively analyze the design space of AI-based recommender systems, researchers have categorized these systems into different architectural layers, each responsible for specific aspects of recommendation.

At the Data Collection and Preprocessing Layer, researchers focus on gathering and preprocessing vast amounts of user data to build comprehensive user profiles. Techniques like collaborative filtering and content-based filtering have been explored to analyze user preferences and item characteristics (Reference: Smith et al., 2022; Johnson and Brown, 2023).

Moving up the architectural layers, the Feature Engineering and Representation Learning Layer aims to extract meaningful features and embeddings from the data. Deep Learning models like Neural Collaborative Filtering (NCF) and Transformer-based architectures have gained attention for their ability to learn rich representations from user-item interactions (Reference: Lee and Kim, 2021; Chen et al., 2022).

The Recommendation Algorithm Layer is responsible for developing algorithms that generate personalized recommendations based on user preferences. Recent advancements include hybrid recommendation techniques that combine collaborative and content-based filtering, as well as reinforcement learning approaches for sequential recommendation tasks.

At the Interpretability and Fairness Layer, researchers focus on ensuring transparency and fairness in recommender system outputs. Explainable AI (XAI) techniques and fairness-aware recommendation algorithms have been studied to provide users with insights into the reasons behind recommendations and mitigate potential biases (Reference: Wang et al., 2022; Zhang and Li, 2023).

The Deployment and Evaluation Layer involves the real-world implementation of AI-based recommender systems. Researchers investigate methods to deploy models at scale while considering resource constraints and latency requirements. Moreover, the evaluation of recommender systems includes metrics such as accuracy, diversity, and serendipity to assess overall performance and user satisfaction.

By segregating the design space of AI-based recommender systems into these distinct architectural layers, this survey paper aims to provide readers with an organized understanding of the state-of-the-art approaches in personalized recommendations. These architectural layers serve as a structured framework for exploring the latest advancements and challenges in AI-driven recommendation technologies.

Please note that the references used in this example are imaginary

iv. Major Approaches in a Specific Domain:

Every domain usually has two to three major classes on which all the issues in that domain are addressed or the advancements in that domain are identified.

 For example,

i) In   Software Testing: Black box or White box testing

ii) In Networking:  Wired or Wireless  Networking and

iii) In Image processing Spatial or Temporal based Image Processing etc.

iv) In Telcom it is 2G, 3G,4G and 5G etc.

Literature Survey: Major Classes in Telecommunication Technology – 2G, 3G, 4G, and 5G

The domain of Telecommunication Technology has witnessed significant advancements over the years, leading to the emergence of major classes based on different generations of mobile communication systems. This survey paper aims to explore and analyze the key characteristics and advancements of each major class – 2G, 3G, 4G, and 5G.

At the inception of mobile communication, 2G (Second Generation) technology marked the transition from analog to digital communication. Researchers have extensively studied various 2G technologies, including GSM (Global System for Mobile Communications) and CDMA (Code Division Multiple Access) to provide basic voice and text messaging services (Reference: Smith et al., 2016; Johnson and Brown, 2018).

The evolution of mobile communication led to the introduction of 3G (Third Generation) technology, which brought about significant improvements in data transfer capabilities. With 3G technologies like UMTS (Universal Mobile Telecommunications System) and EV-DO (Evolution-Data Optimized), researchers explored higher data speeds, enabling services like mobile internet browsing and video streaming.

Subsequent advancements led to the deployment of 4G (Fourth Generation) technology, revolutionizing the mobile communication landscape. LTE (Long-Term Evolution) and WiMAX (Worldwide Interoperability for Microwave Access) are examples of 4G technologies that offer high-speed data transfer, low latency, and enhanced network capacity (Reference: Lee and Kim, 2020; Chen et al., 2022).

Currently, the telecommunications industry is witnessing the widespread adoption of 5G (Fifth Generation) technology, promising even more transformative capabilities. Researchers have delved into mmWave (millimeter-wave) frequencies, Massive MIMO (Multiple-Input Multiple-Output), and Network Slicing, enabling ultra-fast data speeds, low latency, and supporting the Internet of Things (IoT) (Reference: Wang et al., 2022; Zhang and Li, 2023).

This survey paper aims to provide readers with a comprehensive understanding of the major classes in Telecommunication Technology – 2G, 3G, 4G, and 5G. By analyzing the key characteristics and advancements of each generation, researchers can gain insights into the technological progression that has shaped modern mobile communication systems.

v.  History of Development:

Some research domains like  Cloud Computing, Big Data Management, Mobile Technology, Television technology etc. are linear. Such developments can be explained in chronological order.

One can find various options for a selected domain of research and it is this organization that is the challenging part of writing a survey paper.

The following points are to be elaborated for each paper which is surveyed as a part of writing the survey paper.

–  What are you going to say about the paper under consideration? –  Research direction of the paper – Methods, mathematical modelling and approach or algorithms used to solve the problem: eg.  Fuzzy logic, Gaussian process, neural network etc.

– Whether the paper consider theoretical issues of the concept or solves any application using the concept?

– Is the paper considered the continuation of another work? is it an improvement on another work?

– How validation of work is done i)through theoretical proofs?  ii) simulation? iii)hardware test bed? or iv) real-life deployment?

– How is the work compared with other methods? and under what circumstances does the method under consideration perform better?

-On what parameters the paper under consideration stands apart from other papers like i)higher performance? ii) higher robustness? iii) lower computational complexity?

The author of each survey paper must be acknowledged by citing the paper referred to.  In your survey do indicate the author names as well: Graham and Bell [3] have identified the importance of training, Patric et.al. [3] developed a simple methodology etc.

There are two reasons for this. One is the gratitude towards the authors, to whose work you are referring. Second, your reader will come to know the core people in the area in which he intends to carry out his future research. It is always good to mention in which particular country/University/Lab the work was carried out.

Survey Paper: Advancements in Cloud Computing: A Chronological Perspective

Cloud Computing has emerged as a transformative technology, revolutionizing the way computing resources are provisioned and utilized. This survey paper aims to explore the chronological advancements in Cloud Computing, focusing on key research directions, methodologies, and comparisons with other methods.

Paper 1: “Virtualization in Cloud Computing” – John et al. [1]

• In this paper, John et al. present the concept of virtualization and its application in Cloud Computing.
• Research Direction: The paper emphasizes the benefits of virtualization in enabling multi-tenancy and resource isolation in cloud environments.
• Methods: The paper discusses various virtualization techniques, including full virtualization and para-virtualization, to optimize performance and resource utilization.
• Application: The paper showcases how virtualization facilitates the seamless deployment of multiple applications on a shared physical infrastructure.

Paper 2: “MapReduce: Simplified Data Processing on Large Clusters” – Dean and Ghemawat [2]

• This influential paper introduces the MapReduce programming model for the efficient processing of large-scale data in Cloud Computing.
• Research Direction: The paper focuses on distributed data processing, fault tolerance, and scalability in Cloud environments.
• Methods: The MapReduce paradigm leverages parallelization and fault tolerance to process massive datasets.
• Application: The paper demonstrates how MapReduce can efficiently perform data-intensive tasks, such as web indexing and log processing.

Paper 3: “Machine Learning as a Service (MLaaS) in Cloud Computing” – Smith and Patel [3]

• In this work, Smith and Patel explore the concept of providing Machine Learning capabilities as a service in the Cloud.
• Research Direction: The paper delves into the integration of Machine Learning algorithms in Cloud platforms to enable MLaaS.
• Methods: The paper discusses various ML algorithms, including neural networks and decision trees, used for predictive analytics.
• Application: The paper showcases real-life deployments of MLaaS for applications like fraud detection and sentiment analysis.

Paper 4: “Serverless Computing: The Next Paradigm Shift in Cloud Architecture” – Lee et al. [4]

• Lee et al. present the concept of serverless computing and its potential impact on Cloud architecture.
• Research Direction: The paper explores the benefits of serverless computing in terms of cost-efficiency and scalability.
• Methods: The paper explains the use of Function as a Service (FaaS) to deploy event-driven applications without managing server infrastructure.
• Application: The paper highlights the practical applications of serverless computing for IoT data processing and real-time data analytics.

Through theoretical analysis and simulation studies, these papers validate their proposed methodologies and demonstrate the effectiveness of their approaches. Each work compares its method with existing techniques, highlighting higher performance and lower computational complexity in specific scenarios (Reference: Johnson and Brown [5], Chen et al. [6]).

The acknowledgement of the authors’ contributions is essential to show gratitude and establish recognition within the research community. John et al. [1] conducted their work at the University of XYZ, while Lee and Patel [3] carried out their research at ABC Labs.

This chronological survey paper aims to provide readers with a comprehensive understanding of the evolution of Cloud Computing, the core research directions, and the key contributors in this domain.

6. The “Conclusion” Section of the Survey Paper

The conclusion must answer the queries presented by your survey goals and objectives. The conclusion must be written in an interesting yet academic manner.  No emotions should be attached to your conclusions but a commentary in the third person is required.  Being the final portion of your survey paper, the conclusion serves as the researcher’s final say on the subject of the survey.

The conclusion must be a synthesis of the survey results with

i) an interpretation of each result

ii) the proposal of a course of action based on the result and

iii) a solution to the issues that emerged from the survey.

The tone of the conclusion should match that of the results and the rest of the data collection process.  The conclusion should be able to wrap up the entire survey from the formulation of survey goals up to the satisfaction of such objectives.

After roughly two decades of research on leaf image analysis for pathological issues,  many elements of pathological issues associated with leaves are well understood. In particular, accurate and efficient algorithms for leaf-diseased spot extraction are now well known.

As a result, during the past few years, we have seen the focus turn from the fundamentals of disease spot extraction to more difficult problems such as, the type of the leaf disease and the stage of the leaf disease.  Few algorithms in this context are available. However, a comprehensive evaluation and comparison of these more advanced algorithms has yet to be done.

One of our goals in this review is to consolidate existing quantitative results and to carry out comparative analyses. We believe that much of the leaf image analysis for pathological work in the coming decade should and will be bolstered by more complete quantitative performance evaluations. The recent article by Wimar [10] is a promising first step.

Perhaps the most practically significant advance in the last decade has been the appearance of machine learning algorithms. However current implementation of Machine learning algorithms is still relatively simplistic. More demanding potential applications require algorithms to be very precise and reliable. This remains a challenging research topic that we predict will see progress in the coming decade.

For further details on writing the conclusion section, you can visit my blog post on :  Art of Writing Conclusion Section to your Research Paper

Example of a Survey Paper for a Specific Domain

How to write a survey paper in computer science domain.

Along with my research scholar, I have written a survey paper which is published in one of the most popular journals in the computer science domain. Please visit the link for the full survey paper.

Systematic analysis of satellite image-based land cover classification techniques: literature review and challenges, February 2019, International Journal of Computers and Applications 43(4):1-10

Doi: 10.1080/1206212x.2019.1573946.

Before We Close….

While writing a blog post, I realized I couldn’t cover everything about crafting survey papers. That’s where “WRITING LITERATURE SURVEY PAPER: A STEP BY STEP GUIDE” comes in. It’s an incredible book that simplifies the writing process using a single example, “The Role of Artificial Intelligence in Healthcare Diagnostics.” Imagine having a friendly mentor explaining each step. Although a blog post can’t teach everything, this book breaks it down so you can become a survey paper writing expert.

A survey paper plays a crucial role in providing readers with a systematic overview of existing research, methodologies, and advancements in a specific field. By adhering to the outlined steps, the survey paper can effectively convey valuable insights and contribute to the understanding of the chosen research domain.

Frequently Asked Questions:

How many references do i need for 2000 words survey paper.

  At least 10 quality publications need to be referred for a 2000-word survey paper.

Whether survey papers are counted for the Ph.D. submission?

No. Survey papers are not counted for Ph.D. thesis submission.

Whether Scopus indexed journals accept survey papers for publication?

Yes. Many Scopus-indexed journals accept high-quality survey papers for publication.

What is the difference between a Survey Paper and a Review Paper?

In short, a Survey Paper provides a comprehensive summary of existing research on a specific topic, presenting the state-of-the-art and research trends. On the other hand, a Review Paper offers a critical evaluation and analysis of the literature, identifying gaps and suggesting future research directions.

Upcoming Events

• Visit the Upcoming International Conferences at Exotic Travel Destinations with Travel Plan
• Visit for  Research Internships Worldwide

Recent Posts

• These Institutes Offer Remote Research/Academic Internships
• How to Include Your Journal in the UGC-CARE List? A Guide for Publishers
• Understanding UGC CARE Journals: A Comprehensive Guide
• Top 10 AI-Based Research Paper Abstract Generators
• EditPad Research Title Generator: Is It Helpful to Create a Title for Your Research?
• All Blog Posts
• Research Career
• Research Conference
• Research Internship
• Research Journal
• Research Tools
• Uncategorized
• Research Conferences
• Research Journals
• Research Grants
• Internships
• Research Internships
• Email Templates
• Conferences
• Blog Partners
• Privacy Policy

Copyright © 2024 Research Voyage

Design by ThemesDNA.com

How to write a Survey Paper

So you want to write a survey paper? How do you begin?

What is (not) a survey paper?

Some of the following is adapted from https://www.researchgate.net/post/How_to_write_survey_or_review_papers_and_What_sections_should_be_mentioned_in_such_papers

To answer this question its best to first ask what a survey paper is not . A survey paper is not simply a core dump of a bunch of papers in a common area.

Instead, a survey is a research paper whose data and results are taken from other papers. This means that you should have a point to make or some new conclusion to draw.

You’ll make this point or draw these conclusions based upon a broad reading of previous works. You need to really know the topic in order to have the audacity to claim that a thorough survey of the field. You’ll need to be completely aware of the main themes, directions, controversies, and results in the field. You may wish to email and interview authors of related works to get their opinion.

Writing a survey paper is much more difficult than writing a research paper

You do not simply list prior results. You need to assimilate and synthesize the results. Sometimes you’ll need to address conflicts in notation or introduce entirely new notation.

And, of course, you need to have a point. The point you make will determine the organization of survey paper. The structure of the main sections of the paper will reflect the structure of field. You might consider the following organization:

• Simple to complex scale. Maybe there was some seminal invention that people add more and more complexity onto — this is very very very common in AI and ML.
• Comparative Analysis. You compare Two or more different approaches to the same problem.
• Pipeline Analysis. Many complex solutions require a pipeline that you’ll describe and categorize and annotate.
• Disentanglement. Maybe your field has researchers conflating issues that need to be carefully untangled.
• Historical. Tell the story of something if its compelling.

You’ll do a good job if you can communicate a perspective and/or articulate the gaps in the knowledge. This is difficult and should probably not be attempted by young scientists or graduate students.

The bottom line is that you need to have a point to make and, conclusion to draw, or some kind of contribution that is not just a list of abstracts.

An iterative process

The following is taken from https://academia.stackexchange.com/questions/43371/how-to-write-a-survey-paper

The point of a survey paper of the type you are discussion (as distinct from a systematic review), is to provide an organized view of the current state of the field. As such, you should not be attempting to cite every paper, but only the ones that are significant (which will still be an awful lot)

Writing a good survey paper is hard, and there really aren’t any good shortcuts: you  do  need to become familiar with the content of a very large number of papers, in order to make sure that the view you are presenting is sane.

Step 1: Begin by collecting a large pile of papers to survey.

Start by collecting a handful of papers that you are interested in. See who cites them and what they cite.

Step 2: Things of an organization schema.

Based on your experience and a readings, hypothesize an organization schema for the field. What point are you trying to make with this survey?

Start reading (mostly skimming) and organizing your collection of papers you read using this schema, including noting which ones are most important and which do not fit the schema well.

As you find significant numbers of papers that do not fit the schema well, adjust the schema to better fit what you are actually finding and shift the organization of your collection to match.

Step 3: Find new papers

As you continue to read you’ll find papers that cite and are cited by the papers you’ve read. Add these new papers to the “to be read” collection based on the adjusted schema, then return to Step 1.

Convergence.

When the process converges to a stable schema and an empty to-be-read pile, you will have a well-developed view of the current state of the field and be in a good position to write a survey. Note, however, that this may take a number of months.

Hints and Tricks

Use a bib manager. Zotero, Mendeley, etc

Use a consistent bibtex index structure. I use lastnameYearFirstwordoftitle convention

You should follow this process in your PhD study generally, but it doesn’t mean that you have to write a survey paper. A survey paper needs to have something to say; a point to make; or some contribution in the way we think about a thing.

ScienceSphere.blog

Mastering The Art Of Writing A Survey Paper: A Step-By-Step Guide

Table of Contents

Importance of survey papers in academic research

Survey papers play a crucial role in academic research as they provide a comprehensive overview of a specific topic or field. These papers serve as valuable resources for researchers, students, and professionals who want to gain a deeper understanding of a subject. By synthesizing existing literature, survey papers help to identify research gaps, highlight key findings, and offer insights into future research directions.

Survey papers are essential for the following reasons:

Summarizing existing knowledge: Survey papers consolidate and summarize the existing body of knowledge on a particular topic. They provide a comprehensive overview of the research conducted in the field, making it easier for readers to grasp the key concepts and findings.

Identifying research gaps: By analyzing the existing literature, survey papers help researchers identify areas where further investigation is needed. They highlight the gaps in knowledge and suggest potential research questions that can contribute to the advancement of the field.

Saving time and effort: Instead of going through numerous individual research papers, survey papers offer a consolidated source of information. Researchers can save time and effort by referring to a well-structured survey paper that provides a comprehensive understanding of the topic.

Providing a foundation for new research: Survey papers serve as a foundation for new research. They provide researchers with a solid understanding of the existing literature, enabling them to build upon previous studies and contribute to the field’s knowledge.

Purpose of the blog post

The purpose of this blog post is to guide aspiring researchers and students on how to write an effective survey paper. It will provide a step-by-step approach to help them navigate through the process of selecting a topic, conducting a literature review, outlining the structure, writing the paper, editing and proofreading, formatting and presentation, and finalizing the survey paper.

By following the guidelines outlined in this blog post, readers will be equipped with the necessary tools and knowledge to produce a high-quality survey paper that adds value to the academic community. Whether they are writing a survey paper for a course assignment, a research project, or a publication, this blog post will serve as a comprehensive resource to help them excel in their writing endeavors.

In the next section, we will delve into the basics of survey papers, including their definition, different types, and the benefits of writing one.

Understanding the Basics

A survey paper is a comprehensive review of existing literature on a specific topic or research area. It aims to provide a summary and analysis of the current state of knowledge in the field. Understanding the basics of survey papers is crucial for researchers and academics who wish to contribute to the existing body of knowledge. Here, we will explore the definition of a survey paper, different types of survey papers, and the benefits of writing one.

Definition of a survey paper

A survey paper, also known as a review paper or a literature review, is a type of academic paper that synthesizes and analyzes existing research on a particular topic. It goes beyond summarizing individual studies and aims to provide a comprehensive overview of the field. The goal of a survey paper is to identify trends, patterns, and gaps in the existing literature .

Different types of survey papers

There are several types of survey papers, each with its own purpose and focus. Some common types include:

Traditional survey papers : These provide a broad overview of the topic, covering various aspects and subtopics. They aim to present a comprehensive summary of the existing literature.

Focused survey papers : These focus on a specific aspect or subtopic within a broader field. They delve deeper into a particular area of interest and provide a more detailed analysis.

Systematic review papers : These follow a specific methodology for selecting and analyzing studies. They aim to minimize bias and provide an objective assessment of the available evidence.

Meta-analysis papers : These involve statistical analysis of data from multiple studies to draw conclusions and identify patterns or relationships.

Benefits of writing a survey paper

Writing a survey paper offers several benefits for researchers and academics:

Understanding the research landscape : Conducting a comprehensive literature review allows researchers to gain a deep understanding of the current state of knowledge in their field. It helps identify gaps, controversies, and areas that require further investigation.

Contributing to the field : By synthesizing and analyzing existing research, survey papers provide valuable insights and perspectives. They can help shape the direction of future research and contribute to the advancement of knowledge.

Building credibility : Publishing a well-written survey paper enhances the author’s reputation and credibility in the academic community. It demonstrates expertise in the field and the ability to critically evaluate and synthesize existing research.

Identifying research opportunities : Survey papers often highlight areas where further research is needed. They can inspire new research questions and guide researchers towards fruitful avenues of investigation.

In conclusion, understanding the basics of survey papers is essential for researchers and academics. It involves knowing the definition of a survey paper, different types of survey papers, and the benefits of writing one. By conducting a comprehensive literature review and synthesizing existing research, survey papers contribute to the advancement of knowledge in a particular field. They provide valuable insights, identify research gaps, and guide future research directions.

Choosing a Topic

Choosing the right topic is a crucial step in writing a survey paper. It sets the foundation for your research and determines the direction of your paper. Here are some key considerations when selecting a topic:

Identifying a Research Gap

To begin, you need to identify a research gap in the existing literature. Look for areas where there is limited or conflicting information, unanswered questions, or emerging trends. This will ensure that your survey paper adds value to the academic community by filling a knowledge gap .

Selecting a Specific Area of Interest

Once you have identified a research gap, narrow down your focus by selecting a specific area of interest within that gap. Choose a topic that aligns with your expertise and interests . This will make the writing process more enjoyable and allow you to bring a unique perspective to the paper.

Ensuring the Topic is Relevant and Significant

When choosing a topic, it is important to consider its relevance and significance. Select a topic that is timely and has practical implications . This will make your survey paper more valuable to readers and increase its impact. Additionally, consider the potential for future research and the broader implications of your chosen topic.

To ensure the relevance and significance of your topic, you can:

• Review recent publications and conference proceedings to identify emerging trends and hot topics in your field.
• Consult with experts and mentors to get their insights and suggestions on potential topics.
• Consider the practical applications of your chosen topic and how it can contribute to real-world problem-solving.

By following these steps, you can choose a topic that is both interesting to you and valuable to the academic community. Remember, the topic you choose will shape the entire survey paper, so take the time to select it wisely.

In conclusion, choosing a topic for your survey paper involves identifying a research gap, selecting a specific area of interest, and ensuring the topic is relevant and significant. By following these guidelines, you can set the stage for a well-rounded and impactful survey paper.

Conducting a Literature Review

Conducting a thorough literature review is a crucial step in writing a survey paper. It involves searching for relevant sources, evaluating their credibility, and organizing and summarizing the literature. This section will guide you through the process of conducting a literature review effectively.

Searching for relevant sources

When conducting a literature review, it is essential to search for relevant sources that contribute to your understanding of the topic. Here are some tips to help you find the right sources:

Utilize academic databases : Academic databases such as Google Scholar, PubMed, and IEEE Xplore are excellent resources for finding scholarly articles, conference papers, and research studies related to your topic.

Use appropriate keywords : Use specific keywords and phrases that accurately represent your research topic. This will help you narrow down your search and find relevant sources more efficiently.

Explore citation lists : Look for relevant sources in the reference lists of articles and papers you have already found. This can lead you to additional sources that are highly relevant to your research.

Consider different publication types : Apart from academic journals, consider including books, reports, theses, and dissertations in your literature review. These sources can provide valuable insights and perspectives on your topic.

Evaluating the credibility of the sources

It is crucial to evaluate the credibility and reliability of the sources you include in your literature review. Here are some factors to consider when assessing the credibility of a source:

Author’s expertise : Check the credentials and expertise of the author(s) of the source. Look for their affiliations, qualifications, and previous research experience in the field.

Publication venue : Consider the reputation and impact factor of the journal or conference where the source was published. High-quality venues often have a rigorous peer-review process, ensuring the reliability of the research.

Currency of the source : Ensure that the source is up-to-date and reflects the current state of research in the field. This is particularly important in rapidly evolving areas of study.

Peer-reviewed sources : Prefer sources that have undergone a peer-review process. Peer-reviewed articles are evaluated by experts in the field, ensuring the quality and validity of the research.

Organizing and summarizing the literature

Once you have gathered relevant sources, it is essential to organize and summarize the literature effectively. Here are some steps to help you with this process:

Create a citation database : Maintain a database or spreadsheet to keep track of the sources you have found. Include important details such as author names, publication year, title, and relevant notes.

Identify key themes and subtopics : Analyze the literature to identify common themes and subtopics that emerge from the sources. This will help you organize your survey paper and provide a logical flow of ideas.

Summarize the main findings : Write concise summaries of the main findings and key points from each source. Focus on the aspects that are most relevant to your research question or objective.

Identify gaps and controversies : Pay attention to any gaps or controversies in the literature. These can be areas where further research is needed or where different studies present conflicting results.

By following these steps, you can conduct a comprehensive literature review that forms the foundation of your survey paper. Remember to critically analyze and synthesize the information from various sources to provide a balanced and informative overview of the topic.

Outlining the Structure

When writing a survey paper, it is crucial to have a well-structured outline that guides the flow of your content. A clear and organized structure not only helps you present your ideas effectively but also makes it easier for readers to navigate through your paper. In this section, we will discuss the key components of outlining the structure of a survey paper.

The introduction sets the stage for your survey paper and provides essential background information to the readers. It should capture their attention and clearly state the research question or objective of your paper.

Background information : Start by providing a brief overview of the topic and its significance in the field. This helps readers understand the context and relevance of your survey paper.

Research question/objective : Clearly state the main research question or objective that your paper aims to address. This helps readers understand the purpose and focus of your survey.

The main body of your survey paper should be well-organized and structured to present your findings and analysis in a coherent manner. Consider the following points when outlining the main body:

Subtopics and their organization : Identify the key subtopics or themes that you will cover in your survey. These subtopics should be logically organized to provide a smooth flow of ideas. You can use headings and subheadings to clearly indicate the different sections of your paper.

Inclusion of relevant studies and findings : Within each subtopic, include relevant studies, research papers, and findings that contribute to the understanding of the topic. Make sure to cite and reference these sources properly to give credit to the original authors.

The conclusion of your survey paper should summarize the key points discussed in the main body and provide insights for future research directions. Consider the following elements when outlining the conclusion:

Summary of key points : Provide a concise summary of the main findings and insights from your survey. This helps readers grasp the main takeaways from your paper.

Future research directions : Discuss potential areas for further research or gaps that need to be addressed in the field. This encourages readers to explore new avenues and continue the scholarly conversation.

Having a well-structured outline for your survey paper ensures that you cover all the necessary components and present your ideas in a logical and coherent manner. It helps you stay focused and organized throughout the writing process.

Remember to review and revise your outline as needed to ensure that it aligns with the specific requirements and preferences of your survey paper. A well-structured survey paper not only enhances your credibility as a researcher but also contributes to the academic community’s knowledge and understanding of the topic.

Writing the Survey Paper

Writing a survey paper requires careful planning and organization to ensure that the information is presented in a clear and coherent manner. In this section, we will discuss the key steps involved in writing a survey paper.

The introduction of a survey paper plays a crucial role in capturing the reader’s attention and setting the tone for the rest of the paper. It should begin with an engaging opening statement that highlights the importance of the topic. The research question or objective should be clearly stated to provide a roadmap for the paper.

The main body of the survey paper should present a coherent flow of ideas that addresses the research question or objective. It is important to organize the content in a logical manner, using subheadings to divide the paper into sections. Each subtopic should be discussed in detail, providing a comprehensive overview of the existing literature.

When discussing previous studies and findings, it is essential to properly cite and reference the sources. This not only gives credit to the original authors but also adds credibility to the survey paper. Using a consistent citation style throughout the paper is important to maintain uniformity.

The conclusion of the survey paper should summarize the key findings and provide a concise overview of the main points discussed in the main body. It is an opportunity to highlight the significance of the research and its implications for future studies. Recommendations for further research can also be included to encourage future exploration of the topic.

Editing and Proofreading

Once the survey paper is written, it is crucial to thoroughly edit and proofread the content. This involves checking for grammar and spelling errors to ensure clarity and professionalism. It is also beneficial to seek feedback from peers or mentors to gain different perspectives and identify areas for improvement.

Formatting and Presentation

Proper formatting and presentation are essential for a well-structured survey paper. Following the required citation style is crucial to maintain consistency and adhere to academic standards. Headings, subheadings, and paragraphs should be properly formatted to enhance readability. Additionally, including tables, figures, and graphs can help illustrate complex information and enhance the overall presentation of the paper.

Finalizing the Survey Paper

Before submitting the survey paper, it is important to review the overall structure and content. This involves making necessary revisions and improvements to ensure the paper is coherent and cohesive. Proofreading the final version is crucial to eliminate any remaining errors and ensure a polished final product.

In conclusion, writing a survey paper requires careful planning, organization, and attention to detail. By following the steps outlined in this section, you can effectively write a survey paper that contributes to the existing body of knowledge in your field. Mastering the art of writing survey papers will not only enhance your academic research skills but also establish you as a knowledgeable and credible researcher.

Additional Resources:

• Recommended books and articles on survey paper writing

Online tools and platforms for organizing research

References:

List of sources cited in the blog post

Editing and proofreading are crucial steps in the writing process. They ensure that your survey paper is polished, error-free, and effectively communicates your ideas. Here are some essential tips to help you edit and proofread your survey paper effectively:

Checking for grammar and spelling errors

Use grammar and spell-check tools : Utilize grammar and spell-check tools like Grammarly or Microsoft Word’s built-in spell checker to identify and correct any grammatical or spelling errors in your survey paper.

Read your paper aloud : Reading your paper aloud can help you identify awkward sentence structures, grammatical errors, and spelling mistakes that you may have missed while reading silently.

Proofread multiple times : Proofreading is not a one-time task. It is essential to proofread your survey paper multiple times to catch any errors that may have been overlooked during previous rounds of editing.

Ensuring clarity and coherence

Check for clarity of ideas : Ensure that your ideas are presented clearly and concisely. Avoid using jargon or overly complex language that may confuse your readers. Use simple and straightforward language to convey your message effectively.

Maintain coherence and logical flow : Ensure that your survey paper has a logical flow of ideas. Each paragraph should connect smoothly to the next, and there should be a clear progression of thoughts throughout the paper. Use transition words and phrases to guide your readers through the different sections of your survey paper.

Eliminate redundant or irrelevant information : Review your survey paper to identify any redundant or irrelevant information. Remove any content that does not contribute to the overall purpose or argument of your paper. This will help streamline your paper and make it more focused and concise.

Seeking feedback from peers or mentors

Get a fresh pair of eyes : Ask a peer or mentor to review your survey paper. They can provide valuable feedback on areas that may need improvement, such as clarity, organization, or the overall structure of your paper.

Consider different perspectives : When seeking feedback, consider the perspectives of your reviewers. They may offer insights or suggestions that you may not have considered, helping you enhance the quality of your survey paper.

Incorporate feedback effectively : Take the feedback you receive into account and make necessary revisions to your survey paper. Be open to constructive criticism and use it to refine your paper further.

Remember, editing and proofreading are essential steps in the writing process. They help ensure that your survey paper is well-written, error-free, and effectively communicates your research findings. By following these tips, you can enhance the quality and clarity of your survey paper, making it more impactful and engaging for your readers.

Formatting and presentation play a crucial role in the overall quality and readability of a survey paper. Proper formatting ensures that the information is organized and presented in a clear and visually appealing manner. In this section, we will discuss the key aspects of formatting and presentation that you should consider when writing your survey paper.

Following the required citation style

One of the first things you need to consider when formatting your survey paper is the citation style required by your academic institution or the journal you are submitting to. Common citation styles include APA, MLA, and Chicago. Each style has specific guidelines for citing sources, formatting references, and creating in-text citations. It is important to familiarize yourself with the specific requirements of the chosen citation style and consistently apply it throughout your paper.

Properly formatting headings, subheadings, and paragraphs

Headings and subheadings are essential for organizing the content of your survey paper and guiding the reader through the different sections. When formatting headings and subheadings, it is important to follow a consistent hierarchy and formatting style. Typically, main headings are formatted in a larger font size and may be bold or italicized, while subheadings are formatted in a slightly smaller font size. This helps to visually distinguish between different levels of information and makes it easier for the reader to navigate through the paper.

In addition to headings and subheadings, proper formatting of paragraphs is also important. Each paragraph should focus on a single idea or topic and be well-structured with a clear topic sentence and supporting sentences. It is recommended to use a standard font such as Times New Roman or Arial, with a font size of 12 points. Additionally, paragraphs should be indented and have appropriate line spacing to enhance readability.

Including tables, figures, and graphs if necessary

Tables, figures, and graphs can be effective tools for presenting complex data or summarizing key findings in a visual format. When including these elements in your survey paper, it is important to ensure that they are properly labeled and referenced within the text. Tables should have clear column headings and be organized in a logical manner. Figures and graphs should have descriptive captions and be accompanied by a brief explanation in the text.

It is also important to consider the placement of tables, figures, and graphs within the paper. They should be inserted close to the relevant text and be easily accessible to the reader. If necessary, you can also refer to these elements in the text to provide further explanation or analysis.

Formatting and presentation are essential aspects of writing a high-quality survey paper. By following the required citation style, properly formatting headings and paragraphs, and including tables, figures, and graphs when necessary, you can enhance the overall readability and visual appeal of your paper. Remember to consistently apply these formatting guidelines throughout your survey paper to maintain a professional and polished appearance.

After going through the process of conducting a literature review, outlining the structure, writing the survey paper, and editing and proofreading it, you are now ready to finalize your survey paper. This stage involves reviewing the overall structure and content, making necessary revisions and improvements, and proofreading the final version.

Reviewing the overall structure and content

At this stage, it is crucial to review the overall structure and content of your survey paper. Ensure that the paper flows logically and coherently from the introduction to the conclusion. Check if the main body of the paper effectively addresses the research question or objective stated in the introduction. Make sure that each subtopic is adequately covered and that the inclusion of relevant studies and findings supports your arguments.

Making necessary revisions and improvements

During the finalization stage, it is common to identify areas that require revisions and improvements. Pay attention to the clarity and conciseness of your writing. Revise sentences or paragraphs that may be confusing or convoluted . Ensure that your arguments are well-supported by the literature and that you have properly cited and referenced all sources. Eliminate any redundant or irrelevant information that may distract readers from the main points of your survey paper.

Proofreading the final version

Proofreading is a crucial step in finalizing your survey paper. Check for grammar and spelling errors that may have been overlooked during the editing process. Ensure that your paper adheres to the required citation style and that all references are correctly formatted. Read through your paper carefully to ensure clarity and coherence . It may be helpful to read your paper aloud or ask a colleague to review it for you. Their fresh perspective can help identify any remaining errors or areas that need improvement.

By following these steps, you can ensure that your survey paper is of high quality and ready for submission or publication. Finalizing your survey paper requires attention to detail and a commitment to producing a well-structured and well-written piece of academic research.

Remember, the finalization stage is not the end of the writing process. It is always beneficial to seek feedback from peers or mentors to gain different perspectives and identify areas for further improvement. Their insights can help you refine your survey paper and make it even stronger.

In conclusion, finalizing a survey paper involves reviewing the overall structure and content, making necessary revisions and improvements, and proofreading the final version. It is a critical stage in the writing process that ensures your survey paper is polished and ready to be shared with the academic community.

Mastering the art of writing survey papers takes time and practice . By following the steps outlined in this blog post and seeking continuous improvement, you can become proficient in writing survey papers that contribute to the advancement of knowledge in your field.

Additional Resources

To further enhance your understanding of survey paper writing, here are some recommended books and articles:

• [Book] “Writing a Successful Research Paper: A Simple Approach” by Stanley Chodorow
• [Article] “How to Write a Survey Paper” by Martijn van Otterlo

Additionally, there are online tools and platforms available that can assist you in organizing your research and citations:

• [Tool] Zotero: A free, open-source reference management software
• [Platform] Mendeley: A platform for managing and sharing research papers

These resources can provide valuable guidance and support as you continue to develop your skills in writing survey papers.

[List of sources cited in the blog post]

When it comes to writing survey papers, having access to additional resources can greatly enhance your understanding and improve the quality of your work. Here are some recommended books, articles, and online tools that can assist you in the process of writing a survey paper.

Recommended Books and Articles on Survey Paper Writing

Writing a Survey Paper by John W. Chinneck: This book provides a comprehensive guide to writing survey papers, covering topics such as selecting a research topic, conducting a literature review, organizing the paper, and presenting the findings effectively.

How to Write a Survey Paper by Marta Tatu: This article offers practical tips and strategies for writing a survey paper, including advice on structuring the paper, synthesizing information, and avoiding common pitfalls.

The Literature Review: A Step-by-Step Guide for Students by Diana Ridley: Although not specifically focused on survey papers, this book offers valuable insights into conducting a literature review, which is a crucial component of writing a survey paper.

Writing a Successful Research Paper: A Simple Approach by Stanley Chodorow: This book provides guidance on various aspects of academic writing, including how to develop a research question, organize ideas, and present arguments effectively.

Online Tools and Platforms for Organizing Research

Zotero : Zotero is a free reference management tool that helps you collect, organize, and cite your sources. It allows you to easily save and annotate articles, books, and websites, and generate citations in various citation styles.

Mendeley : Mendeley is another popular reference management tool that enables you to organize your research library, collaborate with others, and generate citations and bibliographies. It also offers a social networking feature that allows you to connect with researchers in your field.

Google Scholar : Google Scholar is a powerful search engine that specializes in scholarly literature. It can be a valuable resource for finding relevant articles, books, and conference papers for your survey paper.

Microsoft Word or Google Docs : These word processing tools provide essential features for writing and formatting your survey paper. They offer options for creating headings, subheadings, and tables, as well as tools for spell checking and grammar correction.

Remember, while these resources can be helpful, it is important to critically evaluate the information you find and ensure its relevance and credibility before including it in your survey paper.

In conclusion, writing a survey paper requires careful planning, extensive research, and effective organization of information. By utilizing the additional resources mentioned above, you can enhance your writing skills and produce a high-quality survey paper that contributes to the academic community.

List of sources cited in the blog post:

• Chinneck, J. W. (n.d.). Writing a Survey Paper .
• Tatu, M. (n.d.). How to Write a Survey Paper .
• Ridley, D. (2012). The Literature Review: A Step-by-Step Guide for Students .
• Chodorow, S. (2014). Writing a Successful Research Paper: A Simple Approach .

When writing a survey paper, it is crucial to include a comprehensive list of references to support your claims and provide credibility to your work. The references section serves as a valuable resource for readers who wish to delve deeper into the topic or verify the information presented in your survey paper. Here are some important points to consider when creating the references section:

Ensure that you include all the sources that you have cited throughout your survey paper. This includes academic papers, books, journal articles, conference proceedings, and any other relevant sources that have contributed to your research. Proper citation and referencing are essential to avoid plagiarism and give credit to the original authors.

Formatting the references

Follow the required citation style specified by your academic institution or the journal you are submitting your survey paper to. Common citation styles include APA, MLA, Chicago, and IEEE. Each citation style has specific guidelines for formatting the references, including the order of information, punctuation, and capitalization. Properly formatting your references ensures consistency and makes it easier for readers to locate the sources you have used.

Organizing the references

Arrange the references in alphabetical order by the last name of the first author. If there are multiple authors, list them in the same order as they appear in the original source. Include the title of the paper or article, the name of the journal or book, the publication date, and the page numbers if applicable. Be sure to include all the necessary information to help readers locate the source easily.

There are several online tools and platforms available that can assist you in organizing and managing your research references. These tools help you create and format citations, generate bibliographies, and store your references in a centralized location. Some popular reference management tools include Zotero , Mendeley , and EndNote . These tools not only save time but also ensure accuracy and consistency in your references.

Double-checking the references

Before finalizing your survey paper, it is crucial to double-check the references section for any errors or omissions. Make sure that all the citations are accurate and complete. Verify that the formatting and punctuation are consistent throughout the references section. Proofreading the final version of your survey paper includes reviewing the references to ensure they are correctly formatted and properly cited.

Including a well-organized and accurate references section is essential for any survey paper. It adds credibility to your work and allows readers to explore the sources you have used. By following the guidelines for formatting and organizing your references, you can ensure that your survey paper meets the highest standards of academic integrity.

Writing a Survey Paper: A Comprehensive Guide

A. Importance of survey papers in academic research B. Purpose of the blog post

A. Definition of a survey paper B. Different types of survey papers C. Benefits of writing a survey paper

A. Identifying a research gap B. Selecting a specific area of interest C. Ensuring the topic is relevant and significant

A. Searching for relevant sources B. Evaluating the credibility of the sources C. Organizing and summarizing the literature

A. Introduction 1. Background information 2. Research question/objective B. Main Body 1. Subtopics and their organization 2. Inclusion of relevant studies and findings C. Conclusion 1. Summary of key points 2. Future research directions

A. Introduction 1. Engaging opening statement 2. Clear research question/objective B. Main Body 1. Coherent flow of ideas 2. Proper citation and referencing C. Conclusion 1. Recap of main findings 2. Implications and recommendations

A. Checking for grammar and spelling errors B. Ensuring clarity and coherence C. Seeking feedback from peers or mentors

A. Following the required citation style B. Properly formatting headings, subheadings, and paragraphs C. Including tables, figures, and graphs if necessary

A. Reviewing the overall structure and content B. Making necessary revisions and improvements C. Proofreading the final version

A. Recap of the steps involved in writing a survey paper B. Encouragement to master the art of writing survey papers

A. Recommended books and articles on survey paper writing B. Online tools and platforms for organizing research

A. List of sources cited in the blog post

Note: This outline is a general guide and can be modified or expanded based on the specific requirements and preferences of the blog post.

Writing a survey paper is an essential skill for academic researchers. It allows you to summarize and analyze existing literature on a specific topic, providing valuable insights and identifying research gaps. This comprehensive guide will walk you through the process of writing a survey paper, from choosing a topic to finalizing the paper.

Survey papers play a crucial role in academic research as they provide a comprehensive overview of existing knowledge in a particular field. The purpose of this blog post is to guide you through the process of writing a survey paper effectively.

To start, it’s important to understand the basics of a survey paper. A survey paper is a type of academic article that summarizes and synthesizes existing research on a specific topic. There are different types of survey papers, including literature reviews, systematic reviews, and meta-analyses. Writing a survey paper offers several benefits, such as gaining a deep understanding of the topic, identifying research gaps, and contributing to the academic community.

Selecting the right topic is crucial for writing a successful survey paper. Begin by identifying a research gap in your field of interest. This gap could be an unanswered question or an area that requires further exploration. Once you have identified the research gap, narrow down your focus to a specific area of interest. Ensure that the topic is relevant and significant, as this will determine the impact of your survey paper.

A thorough literature review is the foundation of a well-written survey paper. Start by searching for relevant sources such as research articles, books, and conference papers. Evaluate the credibility of these sources by considering factors like the author’s expertise, the journal’s reputation, and the methodology used. Organize and summarize the literature in a systematic manner, highlighting the key findings and arguments.

A well-structured survey paper is essential for clarity and coherence. The structure typically consists of an introduction, main body, and conclusion. In the introduction, provide background information on the topic and clearly state your research question or objective. The main body should be organized into subtopics, each addressing a specific aspect of the topic. Include relevant studies and findings to support your arguments. Finally, in the conclusion, summarize the key points and suggest future research directions.

When writing the survey paper, pay attention to the introduction, main body, and conclusion. The introduction should engage the reader with an opening statement and clearly state the research question or objective. The main body should have a coherent flow of ideas, presenting the literature in a logical manner. Proper citation and referencing are crucial to acknowledge the original authors and avoid plagiarism. In the conclusion, recap the main findings and provide implications and recommendations for future research.

Editing and proofreading are essential to ensure the quality of your survey paper. Check for grammar and spelling errors, and ensure clarity and coherence in your writing. Seek feedback from peers or mentors to get different perspectives and improve the overall quality of your paper.

Proper formatting and presentation enhance the readability of your survey paper. Follow the required citation style, such as APA or MLA, to ensure consistency. Format headings, subheadings, and paragraphs appropriately to create a clear structure. If necessary, include tables, figures, and graphs to present data effectively.

Before submitting your survey paper, review the overall structure and content. Make necessary revisions and improvements to enhance the clarity and coherence of your paper. Finally, proofread the final version to eliminate any remaining errors.

Writing a survey paper requires careful planning and execution. This guide has provided a step-by-step process to help you write a high-quality survey paper. By mastering the art of writing survey papers, you can contribute to the academic community and advance knowledge in your field.

To further enhance your understanding of survey paper writing, consider exploring recommended books and articles on the topic. Additionally, there are online tools and platforms available that can assist you in organizing your research effectively.

[List the sources cited in the blog post here.]

Unveiling The Shelf Life: How Long Does Citric Acid Last?

Unveiling The True Value: How Much Is Jcoin Worth In Today’s Market?

Rebooting Your Booze: How To Reset Alcohol Content

Quick And Easy: Mastering The Art Of Thawing Tuna

Unveiling The Dynamic Interplay: How Physical And Human Systems Shape A Place

Unlocking The Power: How Long Does It Take For Royal Honey To Activate?

Mastering Residency: A Guide On How To Study Effectively

Mastering The Art Of Die Cast Mold Making: A Step-By-Step Guide

Unveiling The Energy Consumption Of Water Coolers: How Much Electricity Do They Really Use?

Mastering Virtual Reality: Unlocking The Secrets To Altering Your Height

Leave a Comment Cancel reply

Save my name, email, and website in this browser for the next time I comment.

Conducting Survey Research

Surveys represent one of the most common types of quantitative, social science research. In survey research, the researcher selects a sample of respondents from a population and administers a standardized questionnaire to them. The questionnaire, or survey, can be a written document that is completed by the person being surveyed, an online questionnaire, a face-to-face interview, or a telephone interview. Using surveys, it is possible to collect data from large or small populations (sometimes referred to as the universe of a study).

Different types of surveys are actually composed of several research techniques, developed by a variety of disciplines. For instance, interview began as a tool primarily for psychologists and anthropologists, while sampling got its start in the field of agricultural economics (Angus and Katona, 1953, p. 15).

Survey research does not belong to any one field and it can be employed by almost any discipline. According to Angus and Katona, "It is this capacity for wide application and broad coverage which gives the survey technique its great usefulness..." (p. 16).

Types of Surveys

Surveys come in a wide range of forms and can be distributed using a variety of media.

Mail Surveys

Group administered questionnaires, drop-off surveys, oral surveys, electronic surveys.

• An Example Survey

Example Survey

General Instructions: We are interested in your writing and computing experiences and attitudes. Please take a few minutes to complete this survey. In general, when you are presented with a scale next to a question, please put an X over the number that best corresponds to your answer. For example, if you strongly agreed with the following question, you might put an X through the number 5. If you agreed moderately, you might put an X through number 4, if you neither agreed nor disagreed, you might put an X through number 3.

Example Question:

As is the case with all of the information we are collecting for our study, we will keep all the information you provide to us completely confidential. Your teacher will not be made aware of any of your responses. Thanks for your help.

Your Name: ___________________________________________________________

Your Instructor's Name: __________________________________________________

Written Surveys

Imagine that you are interested in exploring the attitudes college students have about writing. Since it would be impossible to interview every student on campus, choosing the mail-out survey as your method would enable you to choose a large sample of college students. You might choose to limit your research to your own college or university, or you might extend your survey to several different institutions. If your research question demands it, the mail survey allows you to sample a very broad group of subjects at small cost.

Strengths and Weaknesses of Mail Surveys

Cost: Mail surveys are low in cost compared to other methods of surveying. This type of survey can cost up to 50% less than the self-administered survey, and almost 75% less than a face-to-face survey (Bourque and Fielder 9). Mail surveys are also substantially less expensive than drop-off and group-administered surveys.

Convenience: Since many of these types of surveys are conducted through a mail-in process, the participants are able to work on the surveys at their leisure.

Bias: Because the mail survey does not allow for personal contact between the researcher and the respondent, there is little chance for personal bias based on first impressions to alter the responses to the survey. This is an advantage because if the interviewer is not likeable, the survey results will be unfavorably affected. However, this could be a disadvantage as well.

Sampling--internal link: It is possible to reach a greater population and have a larger universe (sample of respondents) with this type of survey because it does not require personal contact between the researcher and the respondents.

Low Response Rate: One of the biggest drawbacks to written survey, especially as it relates to the mail-in, self-administered method, is the low response rate. Compared to a telephone survey or a face-to-face survey, the mail-in written survey has a response rate of just over 20%.

Ability of Respondent to Answer Survey: Another problem with self-administered surveys is three-fold: assumptions about the physical ability, literacy level and language ability of the respondents. Because most surveys pull the participants from a random sampling, it is impossible to control for such variables. Many of those who belong to a survey group have a different primary language than that of the survey. They may also be illiterate or have a low reading level and therefore might not be able to accurately answer the questions. Along those same lines, persons with conditions that cause them to have trouble reading, such as dyslexia, visual impairment or old age, may not have the capabilities necessary to complete the survey.

Imagine that you are interested in finding out how instructors who teach composition in computer classrooms at your university feel about the advantages of teaching in a computer classroom over a traditional classroom. You have a very specific population in mind, and so a mail-out survey would probably not be your best option. You might try an oral survey, but if you are doing this research alone this might be too time consuming. The group administered questionnaire would allow you to get your survey results in one space of time and would ensure a very high response rate (higher than if you stuck a survey into each instructor's mailbox). Your challenge would be to get everyone together. Perhaps your department holds monthly technology support meetings that most of your chosen sample would attend. Your challenge at this point would be to get permission to use part of the weekly meeting time to administer the survey, or to convince the instructors to stay to fill it out after the meeting. Despite the challenges, this type of survey might be the most efficient for your specific purposes.

Strengths and Weaknesses of Group Administered Questionnaires

Rate of Response: This second type of written survey is generally administered to a sample of respondents in a group setting, guaranteeing a high response rate.

Specificity: This type of written survey can be very versatile, allowing for a spectrum of open and closed ended types of questions and can serve a variety of specific purposes, particularly if you are trying to survey a very specific group of people.

Weaknesses of Group Administered Questionnaires

Sampling: This method requires a small sample, and as a result is not the best method for surveys that would benefit from a large sample. This method is only useful in cases that call for very specific information from specific groups.

Scheduling: Since this method requires a group of respondents to answer the survey together, this method requires a slot of time that is convenient for all respondents.

Imagine that you would like to find out about how the dorm dwellers at your university feel about the lack of availability of vegetarian cuisine in their dorm dining halls. You have prepared a questionnaire that requires quite a few long answers, and since you suspect that the students in the dorms may not have the motivation to take the time to respond, you might want a chance to tell them about your research, the benefits that might come from their responses, and to answer their questions about your survey. To ensure the highest response rate, you would probably pick a time of the day when you are sure that the majority of the dorm residents are home, and then work your way from door to door. If you don't have time to interview the number of students you need in your sample, but you don't trust the response rate of mail surveys, the drop-off survey might be the best option for you.

Strengths and Weaknesses of Drop-off Surveys

Convenience: Like the mail survey, the drop-off survey allows the respondents to answer the survey at their own convenience.

Response Rates: The response rates for the drop-off survey are better than the mail survey because it allows the interviewer to make personal contact with the respondent, to explain the importance of the survey, and to answer any questions or concerns the respondent might have.

Time: Because of the personal contact this method requires, this method takes considerably more time than the mail survey.

Sampling: Because of the time it takes to make personal contact with the respondents, the universe of this kind of survey will be considerably smaller than the mail survey pool of respondents.

Response: The response rate for this type of survey, although considerably better than the mail survey, is still not as high as the response rate you will achieve with an oral survey.

Oral surveys are considered more personal forms of survey than the written or electronic methods. Oral surveys are generally used to get thorough opinions and impressions from the respondents.

Oral surveys can be administered in several different ways. For instance, in a group interview, as opposed to a group administered written survey, each respondent is not given an instrument (an individual questionnaire). Instead, the respondents work in groups to answer the questions together while one person takes notes for the whole group. Another more familiar form of oral survey is the phone survey. Phone surveys can be used to get short one word answers (yes/no), as well as longer answers.

Strengths and Weaknesses of Oral Surveys

Personal Contact: Oral surveys conducted either on the telephone or in person give the interviewer the ability to answer questions from the participant. If the participant, for example, does not understand a question or needs further explanation on a particular issue, it is possible to converse with the participant. According to Glastonbury and MacKean, "interviewing offers the flexibility to react to the respondent's situation, probe for more detail, seek more reflective replies and ask questions which are complex or personally intrusive" (p. 228).

Response Rate: Although obtaining a certain number of respondents who are willing to take the time to do an interview is difficult, the researcher has more control over the response rate in oral survey research than with other types of survey research. As opposed to mail surveys where the researcher must wait to see how many respondents actually answer and send back the survey, a researcher using oral surveys can, if the time and money are available, interview respondents until the required sample has been achieved.

Cost: The most obvious disadvantage of face-to-face and telephone survey is the cost. It takes time to collect enough data for a complete survey, and time translates into payroll costs and sometimes payment for the participants.

Bias: Using face-to-face interview for your survey may also introduce bias, from either the interviewer or the interviewee.

Types of Questions Possible: Certain types of questions are not convenient for this type of survey, particularly for phone surveys where the respondent does not have a chance to look at the questionnaire. For instance, if you want to offer the respondent a choice of 5 different answers, it will be very difficult for respondents to remember all of the choices, as well as the question, without a visual reminder. This problem requires the researcher to take special care in constructing questions to be read aloud.

Attitude: Anyone who has ever been interrupted during dinner by a phone interviewer is aware of the negative feelings many people have about answering a phone survey. Upon receiving these calls, many potential respondents will simply hang up.

With the growth of the Internet (and in particular the World Wide Web) and the expanded use of electronic mail for business communication, the electronic survey is becoming a more widely used survey method. Electronic surveys can take many forms. They can be distributed as electronic mail messages sent to potential respondents. They can be posted as World Wide Web forms on the Internet. And they can be distributed via publicly available computers in high-traffic areas such as libraries and shopping malls. In many cases, electronic surveys are placed on laptops and respondents fill out a survey on a laptop computer rather than on paper.

Strengths and Weaknesses of Electronic Surveys

Cost-savings: It is less expensive to send questionnaires online than to pay for postage or for interviewers.

Ease of Editing/Analysis: It is easier to make changes to questionnaire, and to copy and sort data.

Faster Transmission Time: Questionnaires can be delivered to recipients in seconds, rather than in days as with traditional mail.

Easy Use of Preletters: You may send invitations and receive responses in a very short time and thus receive participation level estimates.

Higher Response Rate: Research shows that response rates on private networks are higher with electronic surveys than with paper surveys or interviews.

More Candid Responses: Research shows that respondents may answer more honestly with electronic surveys than with paper surveys or interviews.

Potentially Quicker Response Time with Wider Magnitude of Coverage: Due to the speed of online networks, participants can answer in minutes or hours, and coverage can be global.

Sample Demographic Limitations: Population and sample limited to those with access to computer and online network.

Lower Levels of Confidentiality: Due to the open nature of most online networks, it is difficult to guarantee anonymity and confidentiality.

Layout and Presentation issues: Constructing the format of a computer questionnaire can be more difficult the first few times, due to a researcher's lack of experience.

Additional Orientation/Instructions: More instruction and orientation to the computer online systems may be necessary for respondents to complete the questionnaire.

Potential Technical Problems with Hardware and Software: As most of us (perhaps all of us) know all too well, computers have a much greater likelihood of "glitches" than oral or written forms of communication.

Response Rate: Even though research shows that e-mail response rates are higher, Opermann (1995) warns that most of these studies found response rates higher only during the first few days; thereafter, the rates were not significantly higher.

Designing Surveys

Initial planning of the survey design and survey questions is extremely important in conducting survey research. Once surveying has begun, it is difficult or impossible to adjust the basic research questions under consideration or the tool used to address them since the instrument must remain stable in order to standardize the data set. This section provides information needed to construct an instrument that will satisfy basic validity and reliability issues. It also offers information about the important decisions you need to make concerning the types of questions you are going to use, as well as the content, wording, order and format of your survey questionnaire.

Overall Design Issues

Four key issues should be considered when designing a survey or questionnaire: respondent attitude, the nature of the items (or questions) on the survey, the cost of conducting the survey, and the suitability of the survey to your research questions.

Respondent attitude: When developing your survey instrument, it is important to try to put yourself into your target population's shoes. Think about how you might react when approached by a pollster while out shopping or when receiving a phone call from a pollster while you are sitting down to dinner. Think about how easy it is to throw away a response survey that you've received in the mail. When developing your instrument, it is important to choose the method you think will work for your research, but also one in which you have confidence. Ask yourself what kind of survey you, as a respondent, would be most apt to answer.

Nature of questions: It is important to consider the relationship between the medium that you use and the questions that you ask. For instance, certain types of questions are difficult to answer over the telephone. Think of the problems you would have in attempting to record Likert scale responses, as in closed-ended questions, over the telephone--especially if a scale of more than five points is used. Responses to open-ended questions would also be difficult to record and report in telephone interviews.

Cost: Along with decisions about the nature of the questions you ask, expense issues also enter into your decision making when planning a survey. The population under consideration, the geographic distribution of this sample population, and the type of questionnaire used all affect costs.

Ability of instrument to meet needs of research question: Finally, there needs to be a logical link between your survey instrument and your research questions. If it is important to get a large number of responses from a broad sample of the population, you obviously will not choose to do a drop-off written survey or an in-person oral survey. Because of the size of the needed sample, you will need to choose a survey instrument that meets this need, such as a phone or mail survey. If you are interested in getting thorough information that might need a large amount of interaction between the interviewer and respondent, you will probably pick in-person oral survey with a smaller sample of respondents. Your questions, then, will need to reflect both your research goals and your choice of medium.

Creating Questionnaire Questions

Developing well-crafted questionnaires is more difficult than it might seem. Researchers should carefully consider the type, content, wording, and order of the questions that they include. In this section, we discuss the steps involved in questionnaire development and the advantages and disadvantages of various techniques.

Open-ended vs. Closed-ended Questions

All researchers must make two basic decisions when designing a survey--they must decide: 1) whether they are going to employ an oral, written, or electronic method, and 2) whether they are going to choose questions that are open or close-ended.

Closed-Ended Questions: Closed-ended questions limit respondents' answers to the survey. The participants are allowed to choose from either a pre-existing set of dichotomous answers, such as yes/no, true/false, or multiple choice with an option for "other" to be filled in, or ranking scale response options. The most common of the ranking scale questions is called the Likert scale question. This kind of question asks the respondents to look at a statement (such as "The most important education issue facing our nation in the year 2000 is that all third graders should be able to read") and then "rank" this statement according to the degree to which they agree ("I strongly agree, I somewhat agree, I have no opinion, I somewhat disagree, I strongly disagree").

Open-Ended Questions: Open-ended questions do not give respondents answers to choose from, but rather are phrased so that the respondents are encouraged to explain their answers and reactions to the question with a sentence, a paragraph, or even a page or more, depending on the survey. If you wish to find information on the same topic as asked above (the future of elementary education), but would like to find out what respondents would come up with on their own, you might choose an open-ended question like "What do you think is the most important educational issue facing our nation in the year 2000?" rather than the Likert scale question. Or, if you would like to focus on reading as the topic, but would still not like to limit the participants' responses, you might pose the question this way: "Do you think that the most important issue facing education is literacy? Explain your answer below."

Note: Keep in mind that you do not have to use close-ended or open-ended questions exclusively. Many researchers use a combination of closed and open questions; often researchers use close-ended questions in the beginning of their survey, then allow for more expansive answers once the respondent has some background on the issue and is "warmed-up."

Rating scales: ask respondents to rate something like an idea, concept, individual, program, product, etc. based on a closed ended scale format, usually on a five-point scale. For example, a Likert scale presents respondents with a series of statements rather than questions, and the respondents are asked to which degree they disagree or agree.

Ranking scales: ask respondents to rank a set of ideas or things, etc. For example, a researcher can provide respondents with a list of ice cream flavors, and then ask them to rank these flavors in order of which they like best, with the rank of "one" representing their favorite. These are more difficult to use than rating scales. They will take more time, and they cannot easily be used for phone surveys since they often require visual aids. However, since ranking scales are more difficult, they may actually increase appropriate effort from respondents.

Magnitude estimation scales: ask respondents to provide numeric estimation of answers. For example, respondents might be asked: "Since your least favorite ice cream flavor is vanilla, we'll give it a score of 10. If you like another ice cream 20 times more than vanilla, you'll give it a score of 200, and so on. So, compared to vanilla at a score of ten, how much do you like rocky road?" These scales are obviously very difficult for respondents. However, these scales have been found to help increase variance explanations over ordinal scaling.

Split or unfolding questions: begin by asking respondents a general question, and then follow up with clarifying questions.

Funneling questions: guide respondents through complex issues or concepts by using a series of questions that progressively narrow to a specific question. For example, researchers can start asking general, open-ended questions, and then move to asking specific, closed-ended, forced-choice questions.

Inverted funneling questions: ask respondents a series of questions that move from specific issues to more general issues. For example, researchers can ask respondents specific, closed-ended questions first and then ask more general, open-ended questions. This technique works well when respondents are not expected to be knowledgeable about a content area or when they are not expected to have an articulate opinion regarding an issue.

Factorial questions: use stories or vignettes to study judgment and decision-making processes. For example, a researcher could ask respondents: "You're in a dangerous, rapidly burning building. Do you exit the building immediately or go upstairs to wake up the other inhabitants?" Converse and Presser (1986) warn that little is known about how this survey question technique compares with other techniques.

The wording of survey questions is a tricky endeavor. It is difficult to develop shared meanings or definitions between researchers and the respondents, and among respondents.

In The Practice of Social Research , Keith Crew, a professor of Sociology at the University of Kentucky, cites a famous example of a survey gone awry because of wording problems. An interview survey that included Likert-type questions ranging from "very much" to "very little" was given in a small rural town. Although it would seem that these items would accurately record most respondents' opinions, in the colloquial language of the region the word "very" apparently has an idiomatic usage which is closer to what we mean by "fairly" or even "poorly." You can just imagine what this difference in definition did to the survey results (p. 271).

This, however, is an extreme case. Even small changes in wording can shift the answers of many respondents. The best thing researchers can do to avoid problems with wording is to pretest their questions. However, researchers can also follow some suggestions to help them write more effective survey questions.

To write effective questions, researchers need to keep in mind these four important techniques: directness, simplicity, specificity, and discreteness.

• Questions should be written in a straightforward, direct language that is not caught up in complex rhetoric or syntax, or in a discipline's slang or lingo. Questions should be specifically tailored for a group of respondents.
• Questions should be kept short and simple. Respondents should not be expected to learn new, complex information in order to answer questions.
• Specific questions are for the most part better than general ones. Research shows that the more general a question is the wider the range of interpretation among respondents. To keep specific questions brief, researchers can sometimes use longer introductions that make the context, background, and purpose of the survey clear so that this information is not necessary to include in the actual questions.
• Avoid questions that are overly personal or direct, especially when dealing with sensitive issues.

When considering the content of your questionnaire, obviously the most important consideration is whether the content of the questions will elicit the kinds of questions necessary to answer your initial research question. You can gauge the appropriateness of your questions by pretesting your survey, but you should also consider the following questions as you are creating your initial questionnaire:

• Does your choice of open or close-ended questions lead to the types of answers you would like to get from your respondents?
• Is every question in your survey integral to your intent? Superfluous questions that have already been addressed or are not relevant to your study will waste the time of both the respondents and the researcher.
• Does one topic warrant more than one question?
• Do you give enough prior information/context for each set of questions? Sometimes lead-in questions are useful to help the respondent become familiar and comfortable with the topic.
• Are the questions both general enough (they are both standardized and relevant to your entire sample), and specific enough (avoid vague generalizations and ambiguousness)?
• Is each question as succinct as it can be without leaving out essential information?
• Finally, and most importantly, try to put yourself in your respondents' shoes. Write a survey that you would be willing to answer yourself, and be polite, courteous, and sensitive. Thank the responder for participating both at the beginning and the end of the survey.

Order of Questions

Although there are no general rules for ordering survey questions, there are still a few suggestions researchers can follow when setting up a questionnaire.

• Pretesting can help determine if the ordering of questions is effective.
• Which topics should start the survey off, and which should wait until the end of the survey?
• What kind of preparation do my respondents need for each question?
• Do the questions move logically from one to the next, and do the topics lead up to each other?

The following general guidelines for ordering survey questions can address these questions:

• Use warm-up questions. Easier questions will ease the respondent into the survey and will set the tone and the topic of the survey.
• Sensitive questions should not appear at the beginning of the survey. Try to put the responder at ease before addressing uncomfortable issues. You may also prepare the reader for these sensitive questions with some sort of written preface.
• Consider transition questions that make logical links.
• Try not to mix topics. Topics can easily be placed into "sets" of questions.
• Try not to put the most important questions last. Respondents may become bored or tired before they get to the end of the survey.
• Be careful with contingency questions ("If you answered yes to the previous question . . . etc.").
• If you are using a combination of open and close-ended questions, try not to start your survey with open-ended questions. Respondents will be more likely to answer the survey if they are allowed the ease of closed-questions first.

Borrowing Questions

Before developing a survey questionnaire, Converse and Presser (1986) recommend that researchers consult published compilations of survey questions, like those published by the National Opinion Research Center and the Gallup Poll. This will not only give you some ideas on how to develop your questionnaire, but you can even borrow questions from surveys that reflect your own research. Since these questions and questionnaires have already been tested and used effectively, you will save both time and effort. However, you will need to take care to only use questions that are relevant to your study, and you will usually have to develop some questions on your own.

Advantages of Closed-Ended Questions

• Closed-ended questions are more easily analyzed. Every answer can be given a number or value so that a statistical interpretation can be assessed. Closed-ended questions are also better suited for computer analysis. If open-ended questions are analyzed quantitatively, the qualitative information is reduced to coding and answers tend to lose some of their initial meaning. Because of the simplicity of closed-ended questions, this kind of loss is not a problem.
• Closed-ended questions can be more specific, thus more likely to communicate similar meanings. Because open-ended questions allow respondents to use their own words, it is difficult to compare the meanings of the responses.
• In large-scale surveys, closed-ended questions take less time from the interviewer, the participant and the researcher, and so is a less expensive survey method. The response rate is higher with surveys that use closed-ended question than with those that use open-ended questions.

Advantages of Open-Ended Questions

• Open-ended questions allow respondents to include more information, including feelings, attitudes and understanding of the subject. This allows researchers to better access the respondents' true feelings on an issue. Closed-ended questions, because of the simplicity and limit of the answers, may not offer the respondents choices that actually reflect their real feelings. Closed-ended questions also do not allow the respondent to explain that they do not understand the question or do not have an opinion on the issue.
• Open-ended questions cut down on two types of response error; respondents are not likely to forget the answers they have to choose from if they are given the chance to respond freely, and open-ended questions simply do not allow respondents to disregard reading the questions and just "fill in" the survey with all the same answers (such as filling in the "no" box on every question).
• Because they allow for obtaining extra information from the respondent, such as demographic information (current employment, age, gender, etc.), surveys that use open-ended questions can be used more readily for secondary analysis by other researchers than can surveys that do not provide contextual information about the survey population.

Potential Problems with Survey Questions

While designing questions for a survey, researchers should to be aware of a few problems and how to avoid them:

"Everyone has an opinion": It is incorrect to assume that each respondent has an opinion regarding every question. Therefore, you might offer a "no opinion" option to avoid this assumption. Filters can also be created. For example, researchers can ask respondents if they have any thoughts on an issue, to which they have the option to say "no."

Agree and disagree statements: according to Converse and Presser (1986), these statements suffer from "acquiescence" or the tendency of respondents to agree despite question content (p.35). Researchers can avoid this problem by using forced-choice questions with these statements.

Response order bias: this occurs when a respondent loses track of all options and picks one that comes easily to mind rather than the most accurate. Typically, the respondent chooses the last or first response option. This problem might occur if researchers use long lists and/or rating scales.

Response set: this problem can occur when using a close-ended question format with response options like yes/no or agree/disagree. Sometimes respondents do not consider each question and just answer no or disagree to all questions.

Telescoping: occurs when respondents report that an event took place more recently than it actually did. To avoid this problem, Frey and Mertens (1995) say researchers can use "aided recall"-using a reference point or landmark, or list of events or behaviors (p. 101).

Forward telescoping: occurs when respondents include events that have actually happened before the time frame established. This results in overreporting. According to Converse and Presser (1986), researchers can use "bounded recall" to avoid this problem (p.21). Bounded recall is when researchers interview respondents several months or so after the initial interview to inquire about events that have happened since then. This technique, however, requires more resources. Converse and Presser said that researchers can also just try to narrow the reference points used, which has been shown to reduce this problem too.

Fatigue effect: happens when respondents grow bored or tired during the interview. To avoid this problem, Frey and Mertens (1995) say researchers can use transitions, vary questions and response options, and they can put easy to answer questions at the end of the questionnaire.

Types of Questions to Avoid

• Double-barreled questions- force respondents to make two decisions in one. For example, a question like: "Do you think women and children should be given the first available flu shots?" does not allow the responder to choose whether women or children should be given the first shots.
• Double negative questions-for example: "Please tell me whether or not you agree or disagree with this statement. Graduate teaching assistants should not be required to help students outside of class." Respondents may confuse the meaning of the disagree option.
• Hypothetical questions- are typically too difficult for respondents since they require more scrutiny. For example, "If there were a cure for cancer, would you still support euthanasia?"
• Ambiguous questions- respondents might not understand the question.
• Biased questions- For example, "Don't you think that suffering terminal cancer patients should be allowed to be released from their pain?" Researchers should never try to make one response option look more suitable than another.
• Questions with long lists-these questions may tire respondents or respondents may lose track of the question.

Pretesting the Questionnaire

Ultimately, designing the perfect survey questionnaire is impossible. However, researchers can still create effective surveys. To determine the effectiveness of your survey questionnaire, it is necessary to pretest it before actually using it. Pretesting can help you determine the strengths and weaknesses of your survey concerning question format, wording and order.

There are two types of survey pretests: participating and undeclared .

• Participating pretests dictate that you tell respondents that the pretest is a practice run; rather than asking the respondents to simply fill out the questionnaire, participating pretests usually involve an interview setting where respondents are asked to explain reactions to question form, wording and order. This kind of pretest will help you determine whether the questionnaire is understandable.
• When conducting an undeclared pretest , you do not tell respondents that it is a pretest. The survey is given just as you intend to conduct it for real. This type of pretest allows you to check your choice of analysis and the standardization of your survey. According to Converse and Presser (1986), if researchers have the resources to do more than one pretest, it might be best to use a participatory pretest first, then an undeclared test.

General Applications of Pretesting:

Whether or not you use a participating or undeclared pretest, pretesting should ideally also test specifically for question variation, meaning, task difficulty, and respondent interest and attention. Your pretests should also include any questions you borrowed from other similar surveys, even if they have already been pretested, because meaning can be affected by the particular context of your survey. Researchers can also pretest the following: flow, order, skip patterns, timing, and overall respondent well-being.

Pretesting for reliability and validity:

Researchers might also want to pretest the reliability and validity of the survey questions. To be reliable, a survey question must be answered by respondents the same way each time. According to Weisberg et. al (1989), researchers can assess reliability by comparing the answers respondents give in one pretest with answers in another pretest. Then, a survey question's validity is determined by how well it measures the concept(s) it is intended to measure. Both convergent validity and divergent validity can be determined by first comparing answers to another question measuring the same concept, then by measuring this answer to the participant's response to a question that asks for the exact opposite answer.

For instance, you might include questions in your pretest that explicitly test for validity: if a respondent answers "yes" to the question, "Do you think that the next president should be a Republican?" then you might ask "What party do you think you might vote for in the next presidential election?" to check for convergent validity, then "Do you think that you will vote Democrat in the next election?" to check the answer for divergent validity.

Conducting Surveys

Once you have constructed a questionnaire, you'll need to make a plan that outlines how and to whom you will administer it. There are a number of options available in order to find a relevant sample group amongst your survey population. In addition, there are various considerations involved with administering the survey itself.

Administering a Survey

This section attempts to answer the question: "How do I go about getting my questionnaire answered?"

For all types of surveys, some basic practicalities need to be considered before the surveying begins. For instance, you need to find the most convenient time to carry out the data collection (this becomes particularly important in interview surveying and group-administered surveys), how long the data collection is likely to take. Finally, you need to make practical arrangements for administering the survey. Pretesting your survey will help you determine the time it takes to administer, process, and analyze your survey, and will also help you clear out some of the bugs.

Administering Written Surveys

Written surveys can be handled in several different ways. A research worker can deliver the questionnaires to the homes of the sample respondents, explain the study, and then pick the questionnaires up on a later date (or, alternately, ask the respondent to mail the survey back when completed). Another option is mailing questionnaires directly to homes and having researchers pick up and check the questionnaires for completeness in person. This method has proven to have higher response rates than straightforward mail surveys, although it tends to take more time and money to administer.

It is important to put yourself into the role of respondent when deciding how to administer your survey. Most of us have received and thrown away a mail survey, and so it may be useful to think back to the reasons you had for not filling it out and returning it. Here are some ideas for boosting your response rate:

• Include in each questionnaire a letter of introduction and explanation, and a self-addressed, stamped envelope for returning the questionnaire.
• Oftentimes, when it fits the study's budget, the envelope might also include a monetary "reward" (usually a dollar to five dollars) as an incentive to fill out the survey.
• Another method for saving the responder time is to create a self-mailing questionnaire that requires no envelope but folds easily so that the return address appears on the outside. The easier you make the process of completing and returning the survey, the better your survey results will be.
• Follow up mailings are an important part of administering mail surveys. Nonrespondents can be sent letters of additional encouragement to participate. Even better, a new copy of the survey can be sent to nonresponders. Methodological literature suggests that three follow up letters are adequate, and two to three weeks should be allowed between each mailing.

Administering Oral Surveys

Face-To-Face Surveys

Oftentimes conducting oral surveys requires a staff of interviewers; to control this variable as much as possible, the presentation and preparation of the interviewer is an important consideration.

• In any face-to-face interview, the appearance of the interviewer is important. Since the success of any survey relies on the interest of the participants to respond to the survey, the interviewer should take care to dress and act in such a way that would not offend the general sample population.
• Of equal importance is the preparedness of the interviewer. The interviewer should be well acquainted with the questions, and have ample practice administering the survey with mock interviews. If several interviewers will be used, they should be trained as a group to ensure standardization and control. Interviewers also need to carry a letter of identification/authentication to present at in-person surveys.

When actually administering the survey, you need to make decisions about how much of the participants' responses need to be recorded, how much the interviewer will need to "probe" for responses, and how much the interviewer will need to account for context (what is the respondent's age, race, gender, reaction to the study, etc.) If you are administering a close-ended question survey, these may not be considerations. On the other hand, when recording more open-ended responses, the researcher needs to decide beforehand on each of these factors:

• It depends on the purpose of the study whether the interview should be recorded word for word, or whether the interviewer should record general impressions and opinions. However, for the sake of precision, the former approach is preferred. More information is always better than less when it comes to analyzing the results.
• Sometimes respondents will respond to a question with an inappropriate answer; this can happen with both open and close-question surveys. Even if you give the participant structured choices like "I agree" or "I disagree," they might respond "I think that is true," which might require the interviewer to probe for an appropriate answer. In an open-question survey, this probing becomes more challenging. The interviewer might come with a set of potential questions if the respondent does not elaborate enough or strays from the subject. The nature of these probes, however, need to be constructed by the researcher rather than ad-libbed by the interviewers, and should be carefully controlled so that they do not lead the respondent to change answers.

Phone Surveys

Phone surveys certainly involve all of the preparedness of the face-to-face surveys, but encounter new problems because of their reputation. It is much easier to hang-up on a phone surveyor than it is to slam the door in someone's face, and so the sheer number of calls needed to complete a survey can be baffling. Computer innovation has tempered this problem a bit by allowing more for quick and random number dialing and the ability for interviewers to type answers programs that automatically set up the data for analysis. Systems like CATI (Computer-assisted survey interview) have made phone surveys a more cost and time effective method, and therefore a popular one, although respondents are getting more and more reluctant to answer phone surveys because of the increase in telemarketing.

Before conducting a survey, you must choose a relevant survey population. And, unless a survey population is very small, it is usually impossible to survey the entire relevant population. Therefore, researchers usually just survey a sample of a population from an actual list of the relevant population, which in turn is called a sampling frame . With a carefully selected sample, researchers can make estimations or generalizations regarding an entire population's opinions, attitudes or beliefs on a particular topic.

Sampling Procedures and Methods

There are two different types of sampling procedures-- probability and nonprobability . Probability sampling methods ensure that there is a possibility for each person in a sample population to be selected, whereas nonprobability methods target specific individuals. Nonprobability sampling methods include the following:

• Purposive samples: to purposely select individuals to survey.
• Volunteer subjects: to ask for volunteers to survey.
• Haphazard sampling: to survey individuals who can be easily reached.
• Quota sampling: to select individuals based on a set quota. For example, if a census indicates that more than half of the population is female, then the sample will be adjusted accordingly.

Clearly, there can be an inherent bias in nonprobability methods. Therefore, according to Weisberg, Krosnick, and Bowen (1989), it is not surprising that most survey researchers prefer probability sampling methods. Some commonly used probability sampling methods for surveys are:

• Simple random sample: a sample is drawn randomly from a list of individuals in a population.
• Systematic selection procedure sample: a variant of a simple random sample in which a random number is chosen to select the first individual and so on from there.
• Stratified sample: dividing up the population into smaller groups, and randomly sampling from each group.
• Cluster sample: dividing up a population into smaller groups, and then only sampling from one of the groups. Cluster sampling is " according to Lee, Forthofer, and Lorimer (1989), is considered a more practical approach to surveys because it samples by groups or clusters of elements rather than by individual elements" (p. 12). It also reduces interview costs. However, Weisberg et. al (1989) said accuracy declines when using this sampling method.
• Multistage sampling: first, sampling a set of geographic areas. Then, sampling a subset of areas within those areas, and so on.

Sampling and Nonsampling Errors

Directly related to sample size are the concepts of sampling and nonsampling errors. According to Fox and Tracy (1986), surveys are subject to both sampling errors and nonsampling errors.

A sampling error arises from the fact that inevitably samples differ from their populations. Therefore, survey sample results should be seen only as estimations. Weisberg et. al. (1989) said sampling errors cannot be calculated for nonprobability samples, but they can be determined for probability samples. First, to determine sample error, look at the sample size. Then, look at the sampling fraction--the percentage of the population that is being surveyed. Thus, the more people surveyed, the smaller the error. This error can also be reduced, according to Fox and Tracy (1986), by increasing the representativeness of the sample.

Then, there are two different kinds of nonsampling error--random and nonrandom errors. Fox and Tracy (1986) said random errors decrease the reliability of measurements. These errors can be reduced through repeated measurements. Nonrandom errors result from a bias in survey data, which is connected to response and nonresponse bias.

Confidence Level and Interval

Any statement of sampling error must contain two essential components: the confidence level and the confidence interval. These two components are used together to express the accuracy of the sample's statistics in terms of the level of confidence that the statistics fall within a specified interval from the true population parameter. For example, a researcher may be "95 percent confident" that the sample statistic (that 50 percent favor candidate X) is within plus or minus 5 percentage points of the population parameter. In other words, the researcher is 95 percent confident that between 45 and 55 percent of the total population favor candidate X.

Lauer and Asher (1988) provide a table that gives the confidence interval limits for percentages based upon sample size (p. 58):

Sample Size and Confidence Interval Limits

(95% confidence intervals based on a population incidence of 50% and a large population relative to sample size.)

• Search Menu
• Sign in through your institution
• Advance articles
• Author Guidelines
• Submission Site
• Open Access
• Why Submit?
• About Journal of Survey Statistics and Methodology
• About the American Association for Public Opinion Research
• About the American Statistical Association
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Journals on Oxford Academic
• Books on Oxford Academic

High-Impact Articles

Journal of Survey Statistics and Methodology , sponsored by the American Association for Public Opinion Research and the American Statistical Association , began publishing in 2013. Its objective is to publish cutting edge scholarly articles on statistical and methodological issues for sample surveys, censuses, administrative record systems, and other related data.

OUP has granted free access to the articles on this page, which represent some of the most cited, most read, and most discussed articles from recent years. These articles are just a sample of the impressive body of research from Journal of Survey Statistics and Methodology .

Simultaneous Estimation of Multiple Sources of Error in a Smartphone-Based Survey

Do i look and sound religious interviewer religious appearance and attitude effects on respondents’ answers, comparing alternatives for estimation from nonprobability samples, most downloaded, the effects of respondent and question characteristics on respondent answering behaviors in telephone interviews, integrating probability and nonprobability samples for survey inference, multiple imputation with survey weights: a multilevel approach, most discussed.

A Review of Conceptual Approaches and Empirical Evidence on Probability and Nonprobability Sample Survey Research

So many questions, so little time: integrating adaptive inventories into public opinion research, web versus other survey modes: an updated and extended meta-analysis comparing response rates.

• Recommend to your Library

Affiliations

• Online ISSN 2325-0992
• Copyright © 2024 American Association for Public Opinion Research
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Training videos   |   Faqs

Writing a Questionnaire Survey Research Paper – Example & Format

In this blog, we will explain how to write a research paper that employs a survey questionnaire. We will discuss all the important points to consider while writing the research paper. The title of our research paper is ‘ Understanding online shopping behaviors of older population – A questionnaire study .’ Please note that this is not a real paper. It is an example paper we put together for the purpose of teaching.

Understanding Online Shopping Behaviors of Older Population – A Questionnaire Study Research Paper Title

1. Introduction

Let’s start with the introduction paragraph. This is where you provide a general overview of the topic. Let’s begin with a strong opening statement. This is normally called a hook since you are trying to hook your readers to your paper. Here we are providing some interesting numbers about the elderly population. We are saying that by the year 2050, more than 30% of the world’s population will be over 60.

Then we follow it up with a future prediction. We are saying that the spending power of the elderly population will increase significantly in the next few years. This is a fantastic way to emphasize the importance of the topic. Now with the next statement, we are highlighting the topic’s timeliness. We are saying that this is a hot topic that has not yet been fully explored. 

In comparison to today, the UN predicts that by 2050, more than 31% of world’s population will be 60 or older [1]. It is expected that senior citizen’s purchasing power will reach £50 trillion by 2030. Researchers should therefore be interested in this growing and relatively unexplored segment of the online market. Introduction paragraph

2. Literature review

Now let’s move on to the literature review. A literature review is an overview of previously published works related to your topic. Let’s start with a broad summary of the previous research activity in this field. We are saying that the topic of consumer behavior is well-studied. Then, we are grouping prior research into three main categories. This statement is a very good example of how to condense and summarize the findings of multiple research papers in one sentence.  

There exists a vast amount of literature on the topic of consumer behavior in marketing [1-3]. Existing literature generally explores: personal factors [4-6], psychological factors [7-10], and situational factors [11-13]. There are very limited previous research findings related to the shopping behaviors of the older population. This study, to the best of our knowledge, represents the first attempt to fill the void in the literature. Literature review & Research gap

Now it’s time to establish the research gap. A research gap is an unexplored or understudied area in the literature that you have identified. We are saying that there are very limited studies focusing on the consumer behaviors of the elderly population. And we are trying to address this particular gap in our work. Then we talk about the novelty component of our work. We are saying that, to the best of our knowledge, this is the first study to investigate this particular issue. 

3. Research question

Let’s talk about the research questions. You have to describe what you intend to accomplish in your research. The aim of the study is to better understand the consumer attitudes and behavior of the older population. We will find this out by using a questionnaire survey.

The aim of the current study was to better understand various factors that influence the attitudes and behaviours of older customers. We employed a survey questionnaire for addressing the research questions at hand.   Research aims & method summary

This concludes the introduction section of the research paper and lets us move on to materials and methods. 

4. Materials and methods

Materials and methods section should be written very clearly with a detailed account of the procedure that was followed in the experiment. The information in this section should be adequate for anyone desiring to replicate the study in the future.

4.1. Participant recruitment and Questionnaire administration

First, you have to explain how the participants were recruited for the study and clearly define your target population. In our case, we have decided to use customers over the age of 60 from an online shopping website. Then we have to explain how we selected the participants for the study.

There are many different types of sampling methods. For example, we have Random sampling, Systematic sampling, Convenience sampling, Cluster sampling, and Stratified sampling. You would have noticed people stopping you in shopping malls for a short survey. This is called convenience sampling. In systematic sampling, you pick every 5th or 10th customer from the database. In our case, we used a random sampling method, which means we randomly picked participants from the database.

The database of customers over the age of 60 who agreed to participate in the survey was collected from the Amazon e-retailer webiste. The respondents were selected by using simple random sampling method from the retailer’s database. A link to the survey was emailed to customers of the e-retailer who agreed to participate in the survey in exchange for a discount coupon. Population, sampling and questionnaire adminstration

Now, you have to explain how the questionnaire will be administered. There are so many different ways in which we can do it. We can do this via phone interview, personal interview, written questionnaire, or online questionnaire. All the methods have both advantages and disadvantages. In our case, we used an online questionnaire that was emailed to the customers who agreed to participate in the study. 

4.2. Questionnaire design and development

Let’s explain the number of questions the survey contains. In our case, we had 24 questions covering various topics. Let’s talk about different types of questions. There are different types of questions. For example, we have open-ended questions, close-ended questions, Likert scale questions, rating scale questions, yes/no questions, and text questions.

The questionnaire consisted of 24 questions that covered key issues around shopping behaviour and reasons for shopping. The frequency of shopping was reported as one of three categories: once a week, once a fortnight, and once a month. The confidence in shopping was determined by asking respondents to rate on a five-point Likert scale. The participants were asked to describe the type of things they shop in a free text box. Questionnaire design

Here we are reporting the frequency of shopping in three categories, once a week, once a fortnight, and once a month. This is a close-ended question since the answers are limited to a fixed set of responses. When designing a close-ended question, it is a good idea to provide an extra option to capture the answers that are not available in the choices. Then, we are using the Likert scale to understand how confident the participants felt while shopping online. The Likert scale is a very popular scale used to ask the participants how much they agree or disagree with a particular statement.

We ask respondents to indicate what sort of stuff they typically purchase online. This is an open-ended question, and we use a free text box to capture the answers. The respondents are free to say what they like. This is particularly useful when you don’t know how people are likely to respond to a question. This is also a good option when you don’t want to influence the participants’ responses. The only problem you should bear in mind is that there are so many different ways the open-ended questions could be answered. This makes the analysis process a bit difficult. So, if you decide to use open-ended questions in your survey, make sure you talk to a statistician first. 

Typically, an online questionnaire starts with a short description of the study followed by the survey questions. In the end, you will ask the participants for demographic information such as age and gender. It is also a good idea to provide a free text box so the participants can provide feedback or raise concerns about your study. Finally, thank your participants after completing the questionnaire.

4.3. Questionnaire testing

Normally when you are administering a questionnaire in multiple languages, the questionnaire is first produced in the main language and then translated into multiple languages. In our case, the questionnaire was first produced in English and then translated into Welsh and Irish. Finally, the translated versions are back-translated into English and checked to make sure there are no discrepancies. Make sure you do a pilot survey. This will help you identify any potential problems in your questions and allow you to fix them before it is too late. Keep repeating the pilot study until you are happy with the questionnaire. 

The questionnaire was developed in English and was then translated into Welsh and Irish. The questionnaire survey was pretested among a group of experts to confirm the survey design and text wording. Questionnaire translation & pilot study

Let’s start with the survey response numbers. In our case, the survey response rate was 31.6%. The response rate can be calculated by dividing the number of completed survey responses by the number of people who viewed or started the survey. Then, follow it up with demographic information of the respondents who participated in the study. In our case, we are presenting the demographic data in a table. 

Now let’s look at different ways to report survey data. One simple way of reporting is to provide the percentage of participants who chose a certain response for a question. In our case, 80% of respondents said they shop at least once a week. 

The survey was administered online to 3,000 customers. 950 completed the survey, resulting in a response rate of 31.6%. The details of basic demographic data are provided in Table 1. When asked how frequently they shop online, 80% of respondents replied at least once a week. The average response was “4: Fairly confident” for the overall rating of the confidence of shopping online. Overall, the majority of the participants stated that they shop for everyday items online, and only a small proportion indicated seasonal products. In conclusion, this research significantly improved our current understanding of shopping behaviors of older population. Results

The question about the confidence of shopping was on the Likert scale. Here, we are saying that the participants’ average response was “Fairly confident.” I must mention that there are so many other ways you can present Likert scale data. For example, you can visualize the entire distribution and present it as a figure in your paper. 

Another way of reporting data is by using broad generic terms such as majority, minority, a large proportion, small proportion, etc. You can do this, but be careful because different readers might interpret these terms differently. Let’s finish up the paper with a conclusion statement that nicely summarizes our work and key contributions. 

Similar Posts

Research Paper Structure – Main Sections and Parts of a Research Paper

In this blog, we explain various topics and sub-topics to be included under each section of a research paper via word cloud diagrams.

How to Create a Research Paper Outline?

In this blog we will see how to create a research paper outline and start writing your research paper.

Writing a Medical Clinical Trial Research Paper – Example & Format

In this blog, we will teach you step-by-step how to write a clinical trial research paper for publication in a high quality scientific journal.

Critical Literature Review : How to Critique a Research Article?

In this blog, we will look at how to use constructive language when critiquing other’s work in your research paper.

How to Write a Research Paper? A Beginners Guide with Useful Academic Phrases

This blog explains how to write a research paper and provides writing ideas in the form of academic phrases.

How to Handle Negative Results in your Research Paper?

In this blog, we will see how to effectively communicate negative and unexpected findings in your paper with detailed examples.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• 7 Share Facebook
• 4 Share Twitter
• 6 Share LinkedIn
• 3 Share Email

Steps to Write a Survey Paper/Review Article

What are the steps to write a survey paper/review article is a very common query of all time asked by most of the researchers from search engines. Before, directly going to tips of writing a survey paper one should know what is survey paper/review article?  Review article, Survey paper is synonym, paper, article is also synonym and are used interchangeably. Mostly this is a key research point for the Masters / PhD Students to explore their area of research in depth and have a serious compilation / arrangement of literature which is preferably publishable.

Survey/review paper is an article which summarizes existing papers in an intuitive way, in spite of providing new facts, analysis or experiments. Or “A paper that abridges and sorted out late research brings about a novel way that incorporates and adds understanding to work in the field. A review article accepts general information of the domain; it underscores the characterization of the current writing, creating a viewpoint on the zone, and assessing patterns.” They are usually published in the review journals and are written by expert researchers in that area of research. Mostly, journal chief editors invite survey papers from the expert researchers in a specific field, but they can also be written and published by new researchers if quality is not compromised.

One questions comes to mind that what is the benefit of writing a review article? A review article/survey paper is a service towards the scientific community; it means that you are writing paper for them. As, it makes easy for new researchers or the researchers who want to change their area of research or want to work on some new area by providing summary of existing papers by stating their advantages and disadvantages and future research directions. Majority of researchers write survey paper after consulting different papers but most important thing is to understand the scientific topic, its flow and future insights. You must make sure that main subject area is going to be covered in your review paper. Moreover, make sure to write new and original review paper instead of copying someone else’s idea, see if there is any existing survey paper on the same or similar topic, then first point out the limitations of that paper and then state your review article contributions. Survey/review paper can be written individually as well as by a group of researchers.

What are the objectives/ingredients of Survey paper?  Provide readers with a perspective of existing work that is overall well composed, thorough and far reaching

• All points of interest must be incorporated, which one’s ought/should think about.
• Make beyond any doubt to cover all existing material
• Logical structure of association among existing studies should be provided
• Summarize each logical structure part in 5-8 papers on a specific sub-category
• State of the craft view of all existing papers by mentioning their advantages and disadvantages with other existing papers or provide a discriminating appraisal of the work that has been carried out
• Include your critique on the hugeness of the methodology and the results introduced in each paper
• Include a dialog on future directions and interesting challenges in different area of that specific domain

Things to be Ponder:

• Everything you compose in review paper must be in your words. There is a common misconception that you can use other wordings while writing a review article. You need to discuss ideas in your own words also must provide reference
• In case there are definitions you can write then in “” and provide proper references to them.
• All thoughts, rewards of other individuals’ words must be accurately credited in the text of the paper and in the references

How to Pick Articles to include in a review article?

When picking papers to peruse – attempt to:

• Pick a latest review of the field so you can rapidly pick up an outline
• Pick a paper that can provide reasonable materials and long clarification, even though they may not be cutting-edge papers sometime
• Pick papers that are cited in references of each other in the same field, so you can compose a significant study out of them
• Include papers from well-known Journals, conferences and workshops
• Include “first” or “foundational” or State of the art papers in the field (as demonstrated in other existing papers
• Include latest papers
• Include older paper also but see they should have been having a good number of citations provided in google scholar

There are different steps to write a survey paper/review article that we will discuss below.

Title Page of Survey Paper:

The title page of survey paper should include following information.

• The main topic of the survey or review paper
• Survey or review keyword
• If you are working in a group than make sure to write names of the group members with their affiliation and email addresses
• It should cover abstract but make sure to write your abstract of minimum 150 to maximum 300 words. The abstract should provide the motivation or need for writing a survey and key contributions of your survey paper
• Three to six keywords which are related to core material of that survey

Introduction:

Your introduction should explain about the background and motivation why you selected the certain topic. Afterwards, summarize your research domain in a precise manner. then summary of existing proposed approaches to the problem and conclude the results. You should discuss the limitation of any existing survey article on same or similar kind of topic to show the need of your survey article. Then you should provide 3-5 key contributions of your work. It’s also good to provide a diagram to show the flow of research in the topic discussed in the survey article, preferably a chronological pictorial representation of the methods or approaches with respect to some logical categorization (See example https://www.researchgate.net/publication/215904200_Probabilistic_Topic_Models_Survey ). Make sure not to forget any step in the introduction while writing review or survey paper.

Concepts and Terminologies:

Survey paper is like a small book on a specific topic and you want the reader to read it and get all the things are one place just like a big shopping mall. So you should provide the key concepts and terminologies related to the topic of the survey. Example of this is provided on link below.

https://www.researchgate.net/publication/215904200_Probabilistic_Topic_Models_Survey

Methods or Approaches:

This is very important part of your review article. You need to categorize existing papers and then create a logical flow in each category. You must discuss the main idea, proposed method, data sets, data variables, results, findings and limitations of each method or group of methods. You can do this for each paper individually or for group of similar methods also. One of very important thing is to provide summary tables for each category. Only writing summary of methods in paragraphs is nothing which is useful or will convince the reviewer to accept your paper at a high-quality journal, conference or workshop. Make sure to explain your body work (whole method, process) in a vivid and clear manner that anyone can understand later on. You can also add about your personal experience while working on your selected approaches, it’s totally up to you. A very nice example can be seen in the following paper.

https://www.researchgate.net/publication/322275289_Ranking_authors_in_academic_social_networks_A_survey

Datasets and Performance Measures:

This section provides the datasets used in that specific research domain. A summary statistic of each dataset can be of great importance to make it useful for readers. Performance measurement of testing of any proposed framework / method / technique /approach is a must in most of the papers. There are different performance evaluation methods adopted for different solutions. Providing the topic specific performance measures can be a very nice addition to a survey paper. (See example in the following paper

https://www.researchgate.net/publication/215904200_Probabilistic_Topic_Models_Survey ).

Future Challenges and Directions:

This part of survey paper is hear of the paper. The limitations are identified by different papers for other paper or even by themselves. They are usually stated in the literature review or papers or in the conclusions and future directions part. It’s a must to provide several challenging future directions based on the limitations in existing papers. Its good to make subheadings in this section with each subheading for a specific limitation. Its also important to provide the references to the papers who provided this limitation. In case the limitations if provided by you then there is no need to give any reference but that is a rare case in most of the survey articles. The future directions should be useful and clear. Let us say you want to work on that area then you should be the first person to select one of the future direction from your own written survey and then providing the solution for that specific challenge or limitation. A review article without clear future challenges can not be published at high quality venue and is only suitable for dustbin, harsh but you know truth always hurts. (See example survey paper for writing future challenges and directions https://www.researchgate.net/publication/310711981_Modelling_to_identify_influential_bloggers_in_the_blogosphere_A_survey )

Conclusion:  

Explain what you can conclude from your research but keep it precise. Based on the sections you have written so far tell the reader what can you conclude for the summary tables you have made. Also give few tips for other researchers that can help them in future. Conclusion should not be more than one or two paragraphs.

References: Make sure to list out all the sources in the reference section of your review paper. Finally, citing references in the text should be done carefully. Avoid adding unknown papers, old but less cited papers, false and fake references in your survey paper because it can demolish your whole effort easily. Remember, a good survey paper written on a mature research area should have atleast 50 references, although a survey paper written on new or emerging topic can be with less than 50 references.  Rules for Citing a Reference are as follows.

• All authors name
• Title of Paper
• Details about Paper, such as, journal (name, volume, issue, pages), conference (name, pages), workshop (name, pages)
• Year of publishing, etc.
• In case you provide a web URL in reference in brackets provide its visiting date also.

Keep in mind you must sometime follow specific referencing style depending upon the venue or publisher requirement, but still you will need to provide complete details as above in that format.

Please keep in mind that steps to write a survey paper/review article are completely different form writing a research paper/article which is not a survey/review. Steps to write a survey paper/review article are provided in detail as well the necessary information related to it. In case you have some tips to further improve the survey paper, please do share with us. As scientific process of doing research can vary a bit for different domains so its really nice if other researchers can benefit from your experiences.

Share this:

• Share on Tumblr

14 Comments Already

thanx,it was nice!!!

Thanks it’s very useful but can you give us an example

Below are 3 example survey papers. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=k6XkGhcAAAAJ&citation_for_view=k6XkGhcAAAAJ:9yKSN-GCB0IC https://scholar.google.com/citations?view_op=view_citation&hl=en&user=k6XkGhcAAAAJ&sortby=pubdate&citation_for_view=k6XkGhcAAAAJ:isC4tDSrTZIC https://scholar.google.com/citations?view_op=view_citation&hl=en&user=k6XkGhcAAAAJ&sortby=pubdate&citation_for_view=k6XkGhcAAAAJ:TQgYirikUcIC

Thanks, it is very useful

thank u very much. the article is very useful

Thanks that was very useful

Thanks. Very well explained and very useful.

you are very welcome.

Thank you very much .your guidelines are appreciable.

Nice to read..very helpful for beginners.

Very welcome Debasish Please share with fellows if you think it will be helpful to them as well.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Notify me of follow-up comments by email.

Notify me of new posts by email.

Academic survey questions, examples, and a step-by-step guide

Use SurveyPlanet for your academic research and gather real data that supports your thesis.

If you want to deepen your knowledge about a particular subject by testing theories, then academic surveys are invaluable. Coming up with a hypothesis is hard enough and a well-designed survey—with carefully constructed questions—will help you test how viable your hypothesis actually is.

The best survey tool for academic research

SurveyPlanet is a great tool for creating academic surveys that will let you put theoretical knowledge into practice and learn by doing. With dozens of templates that include pre-written questions, you will learn right away what a great academic survey should look like.

You’ll also find powerful features like question branching, survey-length estimation, four chart types to display results, and many more when you sign up for a SurveyPlanet account to create an academic survey. Our user-friendly designs will make both creating and filling out surveys a simple yet exciting experience for both you and your respondents.

A step-by-step guide to creating a successful academic survey

• First, gather your thoughts by putting them on paper in order to create a strategy for a successful academic survey.
• Set one ultimate goal, then divide this into smaller, actionable steps that will lead to achieving it.
• Consider your hypothesis and figure out how an academic survey will help you confirm it.
• Before creating questions, decide or learn who will be your target group.
• After you know your target group, think about how big your sample should be to produce statistically significant data. You can do that with our free survey sample size calculator.
• Whether you need one hundred or one thousand responses, think about where your target sample hangs out and how you’ll distribute the survey. Share a survey link with different communities on social media and reach out to friends and acquaintances. Maybe they aren’t your target group, but a friend of a friend is. Don’t get discouraged, people love to help with academic research.
• Books are one thing, real-life data is another. Think about implementing what you already know in your academic survey and how that knowledge can serve a higher purpose.
• When writing questions make sure to use different types, such as multiple choice, Likert scale, open-ended, image choice, and ranking questions. This will make your survey more engaging and fewer people will drop out because they didn't make it through to the end.
• Be brief and concise, both with questions and the survey itself. Think about whether some questions are really necessary. Write questions with straightforward language.
• Make sure your survey isn’t too long. That will put respondents off. With a survey length estimate , you don’t have to manually estimate the length, since SurveyPlanet can do that for you.
• Creating an academic survey and gathering data is great, but you also need to analyze the results. Figure out which method you’ll use before distributing the survey and analyze the quantitative data first (because it’s more straightforward).

While we can't promise that, with the help of our survey tools, you will attain academic excellence in no time, we are sure that our tools will provide a valuable service in your research endeavors. In fact, our mission is to facilitate all the stages of conducting research , from ideation to analysis. That is why we made guidelines covering every step of the process, from creating a survey to survey data analysis for better insights.

Creating your academic survey online is one of the least expensive—but effective—ways to gather all the data you need. People are more eager to respond to an online survey in their free time, from the comfort of their homes. With SurveyPlanet as your partner, you will garner a high response rate and much useful data.

Academic surveys questions and examples

The quality of questions directly influences the quality of data. At the end of the day, it’s the quality of results that matter. Because of that, we pay special attention to creating academic survey questions that are useful for both students and respondents. Academic surveys usually require some demographic questions , including:

• Please select your age range:
• 18 or younger
• Please select your gender:
• What’s your marital status?
• What’s the highest level of education you’ve completed?
• Less than high school
• High school
• Bachelor degree
• Masters degree
• Which category best describes your employment status?
• Employed full-time (40 hours a week or more)
• Employed part-time (less than 40 hours a week)
• Which category best fits the yearly household income of every member combined?
• $20,000-$59,999
• $60,000-$99,999
• $100,000-$149,999
• $150,000 or more

Academic surveys can explore and research many different topics. It just depends on what your area of interest is. Here are some academic survey examples to give you a better idea:

Healthcare surveys

With healthcare surveys, you can research patient demographics, figure out how accessible healthcare is, some common issues people encounter, and ways to improve performance.

Read more about healthcare surveys

Education surveys

Student satisfaction is a topic for which there is always more to ask and say. Using education surveys, you can explore students’ habits, assess the quality of their education, and research teachers’ working conditions.

Read more about education surveys

From employee satisfaction to work-life balance, HR surveys are an inexhaustible source for researching and studying workplace issues.

Read more about HR surveys

Brand surveys

Research consumer demographics and how they perceive different brands to draw conclusions based on the data you collect.

Read more about brand surveys

Depending on the theme that is being addressed, this type of questionnaire can be labeled as a:

• postgraduate taught experience survey
• academic performance survey
• academic research survey

Specific questions depend on the subject you’re studying and researching and we have dozens of academic survey templates to choose from.

SurveyPlanet can help you with your academic research. Use our survey tools, which can help you gather valuable data and insights in no time while providing the best experience to your examinees. Just sign up for a SurveyPlanet account and create an academic survey that will be ready for responses.

Sign up now

Free unlimited surveys, questions and responses.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Adv Pract Oncol
• v.6(2); Mar-Apr 2015

Understanding and Evaluating Survey Research

A variety of methodologic approaches exist for individuals interested in conducting research. Selection of a research approach depends on a number of factors, including the purpose of the research, the type of research questions to be answered, and the availability of resources. The purpose of this article is to describe survey research as one approach to the conduct of research so that the reader can critically evaluate the appropriateness of the conclusions from studies employing survey research.

SURVEY RESEARCH

Survey research is defined as "the collection of information from a sample of individuals through their responses to questions" ( Check & Schutt, 2012, p. 160 ). This type of research allows for a variety of methods to recruit participants, collect data, and utilize various methods of instrumentation. Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behavior, surveys are therefore frequently used in social and psychological research ( Singleton & Straits, 2009 ).

Information has been obtained from individuals and groups through the use of survey research for decades. It can range from asking a few targeted questions of individuals on a street corner to obtain information related to behaviors and preferences, to a more rigorous study using multiple valid and reliable instruments. Common examples of less rigorous surveys include marketing or political surveys of consumer patterns and public opinion polls.

Survey research has historically included large population-based data collection. The primary purpose of this type of survey research was to obtain information describing characteristics of a large sample of individuals of interest relatively quickly. Large census surveys obtaining information reflecting demographic and personal characteristics and consumer feedback surveys are prime examples. These surveys were often provided through the mail and were intended to describe demographic characteristics of individuals or obtain opinions on which to base programs or products for a population or group.

More recently, survey research has developed into a rigorous approach to research, with scientifically tested strategies detailing who to include (representative sample), what and how to distribute (survey method), and when to initiate the survey and follow up with nonresponders (reducing nonresponse error), in order to ensure a high-quality research process and outcome. Currently, the term "survey" can reflect a range of research aims, sampling and recruitment strategies, data collection instruments, and methods of survey administration.

Given this range of options in the conduct of survey research, it is imperative for the consumer/reader of survey research to understand the potential for bias in survey research as well as the tested techniques for reducing bias, in order to draw appropriate conclusions about the information reported in this manner. Common types of error in research, along with the sources of error and strategies for reducing error as described throughout this article, are summarized in the Table .

Sources of Error in Survey Research and Strategies to Reduce Error

The goal of sampling strategies in survey research is to obtain a sufficient sample that is representative of the population of interest. It is often not feasible to collect data from an entire population of interest (e.g., all individuals with lung cancer); therefore, a subset of the population or sample is used to estimate the population responses (e.g., individuals with lung cancer currently receiving treatment). A large random sample increases the likelihood that the responses from the sample will accurately reflect the entire population. In order to accurately draw conclusions about the population, the sample must include individuals with characteristics similar to the population.

It is therefore necessary to correctly identify the population of interest (e.g., individuals with lung cancer currently receiving treatment vs. all individuals with lung cancer). The sample will ideally include individuals who reflect the intended population in terms of all characteristics of the population (e.g., sex, socioeconomic characteristics, symptom experience) and contain a similar distribution of individuals with those characteristics. As discussed by Mady Stovall beginning on page 162, Fujimori et al. ( 2014 ), for example, were interested in the population of oncologists. The authors obtained a sample of oncologists from two hospitals in Japan. These participants may or may not have similar characteristics to all oncologists in Japan.

Participant recruitment strategies can affect the adequacy and representativeness of the sample obtained. Using diverse recruitment strategies can help improve the size of the sample and help ensure adequate coverage of the intended population. For example, if a survey researcher intends to obtain a sample of individuals with breast cancer representative of all individuals with breast cancer in the United States, the researcher would want to use recruitment strategies that would recruit both women and men, individuals from rural and urban settings, individuals receiving and not receiving active treatment, and so on. Because of the difficulty in obtaining samples representative of a large population, researchers may focus the population of interest to a subset of individuals (e.g., women with stage III or IV breast cancer). Large census surveys require extremely large samples to adequately represent the characteristics of the population because they are intended to represent the entire population.

DATA COLLECTION METHODS

Survey research may use a variety of data collection methods with the most common being questionnaires and interviews. Questionnaires may be self-administered or administered by a professional, may be administered individually or in a group, and typically include a series of items reflecting the research aims. Questionnaires may include demographic questions in addition to valid and reliable research instruments ( Costanzo, Stawski, Ryff, Coe, & Almeida, 2012 ; DuBenske et al., 2014 ; Ponto, Ellington, Mellon, & Beck, 2010 ). It is helpful to the reader when authors describe the contents of the survey questionnaire so that the reader can interpret and evaluate the potential for errors of validity (e.g., items or instruments that do not measure what they are intended to measure) and reliability (e.g., items or instruments that do not measure a construct consistently). Helpful examples of articles that describe the survey instruments exist in the literature ( Buerhaus et al., 2012 ).

Questionnaires may be in paper form and mailed to participants, delivered in an electronic format via email or an Internet-based program such as SurveyMonkey, or a combination of both, giving the participant the option to choose which method is preferred ( Ponto et al., 2010 ). Using a combination of methods of survey administration can help to ensure better sample coverage (i.e., all individuals in the population having a chance of inclusion in the sample) therefore reducing coverage error ( Dillman, Smyth, & Christian, 2014 ; Singleton & Straits, 2009 ). For example, if a researcher were to only use an Internet-delivered questionnaire, individuals without access to a computer would be excluded from participation. Self-administered mailed, group, or Internet-based questionnaires are relatively low cost and practical for a large sample ( Check & Schutt, 2012 ).

Dillman et al. ( 2014 ) have described and tested a tailored design method for survey research. Improving the visual appeal and graphics of surveys by using a font size appropriate for the respondents, ordering items logically without creating unintended response bias, and arranging items clearly on each page can increase the response rate to electronic questionnaires. Attending to these and other issues in electronic questionnaires can help reduce measurement error (i.e., lack of validity or reliability) and help ensure a better response rate.

Conducting interviews is another approach to data collection used in survey research. Interviews may be conducted by phone, computer, or in person and have the benefit of visually identifying the nonverbal response(s) of the interviewee and subsequently being able to clarify the intended question. An interviewer can use probing comments to obtain more information about a question or topic and can request clarification of an unclear response ( Singleton & Straits, 2009 ). Interviews can be costly and time intensive, and therefore are relatively impractical for large samples.

Some authors advocate for using mixed methods for survey research when no one method is adequate to address the planned research aims, to reduce the potential for measurement and non-response error, and to better tailor the study methods to the intended sample ( Dillman et al., 2014 ; Singleton & Straits, 2009 ). For example, a mixed methods survey research approach may begin with distributing a questionnaire and following up with telephone interviews to clarify unclear survey responses ( Singleton & Straits, 2009 ). Mixed methods might also be used when visual or auditory deficits preclude an individual from completing a questionnaire or participating in an interview.

FUJIMORI ET AL.: SURVEY RESEARCH

Fujimori et al. ( 2014 ) described the use of survey research in a study of the effect of communication skills training for oncologists on oncologist and patient outcomes (e.g., oncologist’s performance and confidence and patient’s distress, satisfaction, and trust). A sample of 30 oncologists from two hospitals was obtained and though the authors provided a power analysis concluding an adequate number of oncologist participants to detect differences between baseline and follow-up scores, the conclusions of the study may not be generalizable to a broader population of oncologists. Oncologists were randomized to either an intervention group (i.e., communication skills training) or a control group (i.e., no training).

Fujimori et al. ( 2014 ) chose a quantitative approach to collect data from oncologist and patient participants regarding the study outcome variables. Self-report numeric ratings were used to measure oncologist confidence and patient distress, satisfaction, and trust. Oncologist confidence was measured using two instruments each using 10-point Likert rating scales. The Hospital Anxiety and Depression Scale (HADS) was used to measure patient distress and has demonstrated validity and reliability in a number of populations including individuals with cancer ( Bjelland, Dahl, Haug, & Neckelmann, 2002 ). Patient satisfaction and trust were measured using 0 to 10 numeric rating scales. Numeric observer ratings were used to measure oncologist performance of communication skills based on a videotaped interaction with a standardized patient. Participants completed the same questionnaires at baseline and follow-up.

The authors clearly describe what data were collected from all participants. Providing additional information about the manner in which questionnaires were distributed (i.e., electronic, mail), the setting in which data were collected (e.g., home, clinic), and the design of the survey instruments (e.g., visual appeal, format, content, arrangement of items) would assist the reader in drawing conclusions about the potential for measurement and nonresponse error. The authors describe conducting a follow-up phone call or mail inquiry for nonresponders, using the Dillman et al. ( 2014 ) tailored design for survey research follow-up may have reduced nonresponse error.

CONCLUSIONS

Survey research is a useful and legitimate approach to research that has clear benefits in helping to describe and explore variables and constructs of interest. Survey research, like all research, has the potential for a variety of sources of error, but several strategies exist to reduce the potential for error. Advanced practitioners aware of the potential sources of error and strategies to improve survey research can better determine how and whether the conclusions from a survey research study apply to practice.

The author has no potential conflicts of interest to disclose.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center

Sample Survey

• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Save to Library
• Last »
• Training Program Follow Following
• Nonresponse In Sample Surveys Follow Following
• Classification and Regression Trees Follow Following
• Case Study Follow Following
• Training and Development Follow Following
• Balanced Scorecard Follow Following
• Applied Statistics Regression Analysis Follow Following
• Double Sampling Follow Following
• Ratio and Regression Estimation Follow Following
• Survey Non-Response Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Publishing
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Survey Sampling Research Paper

View sample Survey Sampling Research Paper. Browse other statistics research paper examples and check the list of research paper topics for more inspiration. If you need a religion research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our research paper writing service for professional assistance. We offer high-quality assignments for reasonable rates.

1. Definition Of Survey Sampling

Survey sampling can be defined as the art of selecting a sample of units from a population of units, creating measurement tools for measuring the units with respect to the survey variables and drawing precise conclusions about the characteristics of the population or of the process that generated the values of the units. A more specific definition of a survey is the following (Dalenius 1985):

Academic Writing, Editing, Proofreading, And Problem Solving Services

Get 10% off with 24start discount code.

(a) A Survey concerns a set of objects comprising a population. One class of population concerns a finite set of objects such as individuals, businesses, and farms. Another, concerns events during a specific time period, such as crime rates and sales. A third class concerns plain processes, such as land use or the occurrence of certain minerals in an area. More specifically one might want to define a population as, for example, all noninstitutionalized individuals 15–74 years of age living in Sweden on May 1, 2000.

(b) This population has one or more measurable properties. Examples of such properties are individuals’ occupations, business’ revenues, and the number of elks in an area.

(c) A desire to describe the population by one or more parameters defined in terms of these properties. This calls for observing (a sample of ) the population. Examples of parameters are the proportion of unemployed individuals in the population, the total revenue of businesses in a certain industry sector during a given time period and the average number of elks per square mile.

(d) In order to get observational access to the population, a frame is needed i.e., an operational representation, such as a list of the population objects or a map of the population. Examples of frames are business and population registers; maps where the land has been divided into areas with strictly defined boundaries; or all n-digit numbers, which can be used to link telephone numbers to individuals. Sometimes the frame has to be developed for the occasion because there are no registers available and the elements have to be listed. For general populations this is done by combining a multi-stage sampling and the listing procedure by letting the survey field staff list all elements in sampled areas only. Other alternatives would be too costly. For special populations, for example, the population of professional baseball players in the USA, one would have to combine all club rosters into one frame. In some surveys there might exist a number of frames covering the population to varying extents. For this situation a multiple frame theory has been developed (see Hartley 1974).

(e) A sample of sampling units is selected from the frame in accordance with a sampling design, which specifies a probability mechanism and a sample size. There are numerous sample designs developed for different survey situations. The situation may be such that the design chosen solves a problem (using multistage sampling when not all population elements can be listed, or when interviewer and travel costs prevent the use of simple random sampling of elements) or takes advantage of the circumstances (using systematic sampling, if the population is approximately ordered, or using stratified sampling if the population is skewed). Every sample design specifies selection probabilities and a sample size. It is imperative that selection probabilities are known, or else the design is nonmeasurable.

(f ) Observations are made on the sample in accordance with a measurement design i.e., a measurement method and a prescription as to its use. This phase is called data collection. There are at least five different main modes of data collection: face-to-face interviewing, telephone interviewing, self-administered questionnaires and diaries, administrative records, and direct observation. Each of these modes can be conducted using different levels of technology. Early attempts using the computer took place in the 1970s, in telephone interviewing. The questionnaire was stored in a computer and a computer program guided the interviewer throughout the interview by automatically presenting questions on the screen and taking care of some interviewer tasks such as keeping track of skip patterns and personalizing the interview. This technology is called CATI (Computer Assisted Telephone Interviewing). Current levels of technology for the other modes include the use of portable computers for face to face interviewing, touch-tone data entry using the telephone key pad, automatic speech recognition, satellite images of land use and crop yields, ‘people meters’ for TV viewing behaviors, barcode scanning in diary surveys of purchases, electronic exchange of administrative records, and Internet. Summaries of these developments are provided in Lyberg and Kasprzyk (1991), DeLeeuw and Collins (1997), Couper et al. (1998) and Dillman (2000). Associated with each mode is the survey measurement instrument or questionnaire. The questionnaire is the result of a conceptualization of research objectives i.e., a set of properly worded and properly ordered questions. The design of the questionnaire is a science of its own. See for example Tanur (1992) and Sudman et al. (1996).

(g) Based on the measurements an estimation design is applied to compute estimates of the parameters when making inference from the sample to the population. Associated with each sampling design are one or more estimators that are functions of the data that have been collected to make statements about the population parameters. Sometimes estimators rely solely on sample data, but on other occasions auxiliary information is part of the function. All estimators include sample weights that are used to inflate the sample data. To calculate the error of an estimate, variance estimators are formed, which makes it possible to calculate standard errors and eventually confidence intervals. See Cochran (1977) and Sarndal et al. (1992) for comprehensive reviews of the sampling theory.

2. The Status Of Survey Research

There are many types of surveys and survey populations that fit this definition. A large number of surveys are one-time surveys aiming at measuring attitudes or other population behaviors. Some surveys are continuing, thereby allowing the estimation of change over time. An example of this is a monthly labor force survey. Typically such a survey uses a rotating design where a sampled person is interviewed a number of times. One example of this is that the person participates 4 months in a row, is rotated out of the sample for the next 4 months and then rotates back for a final 4 months. Other surveys aim at comparing different populations regarding a certain characteristic, such as the literacy level in different countries. Business surveys often study populations where there are a small number of large businesses and many smaller ones. In the case where the survey goal is to estimate a total, it might be worthwhile to deliberately cut off the smallest businesses from the frame or select all large businesses with a probability of one and the smaller ones with other probabilities.

Surveys are conducted by many different organizations. There are national statistical offices producing official statistics, there are university-based organizations conducting surveys as part of the education and there are private organizations conducting surveys on anything ranging from official statistics to marketing. The survey industry employs more than 130,000 people only in the USA, and the world figure is of course much larger. Survey results are very important to society. Governments get continuing information on parameters like unemployment, national accounts, education, environment, consumer price indexes, etc. Other sponsors get information on e.g., political party preferences, consumer satisfaction, child day-care needs, time use, and consumer product preferences.

As pointed out by Groves (1989), the field of survey sampling has evolved through somewhat independent and uncoordinated contributions from many disciplines including statistics, sociology, psychology, communication, education and marketing research. Representatives of these disciplines have varying backgrounds and as a consequence tend to emphasize different design aspects. However, during the last couple of decades, survey research groups have come to collaborate more as manifested by, for instance, the edited volumes such as Groves et al. (1988), Biemer et al. (1991), Lyberg et al. (1997), and Couper et al. (1998). This teamwork development will most likely continue. Many of the error structures resulting from specific sources must be dealt with by multi-disciplinary teams since the errors stem from problems concerning sampling, recall, survey participation, interviewer practices, question comprehension, and conceptualization.

The justification for sampling (rather than surveying the entire population, a total enumeration) is lower costs but also greater efficiency. Sampling is faster and less expensive compared to total enumeration. Perhaps more surprisingly, sampling often allows a more precise measurement of each sampled unit than that possible in a total enumeration. This often leads to sample surveys having quality features that are superior to those of total enumerations.

Sampling as an intuitive tool has probably been used for centuries, but the development of a theory of survey sampling did not start until the late 1800s. Main contributors to this early development, frequently referred to as ‘the representative method,’ were Kiaer (1897), Bowley (1913, 1926), and Tschuprow (1923). Apart from various inferential aspects they discussed issues such as stratified sampling, optimum allocation to strata, multistage sampling, and frame construction. In the 1930s and the 1940s most of the basic methods that are used today were developed. Fisher’s randomization principle was applied to sample surveys and Neyman (1934, 1938) introduced the theory of confidence intervals, cluster sampling, ratio estimation, and two-phase sampling. The US Bureau of the Census was perhaps the first national statistical office to embrace and further develop the theoretical ideas suggested. For example, Morris Hansen and William Hurwitz (1943, 1949) and Hansen et al. (1953) helped place the US Labor Force Survey on a full probability-sampling basis and they also led innovative work on variance estimation and the development of a survey model decomposing the total survey mean squared error into various sampling and bias components. Other important contributions during that era include systematic sampling (Madow and Madow 1944), regression estimation (Cochran 1942), interpenetrating samples (Mahalanobis 1946) and master samples (Dalenius 1957). More recent efforts have concentrated on allocating resources to the control of various sources of error i.e., methods for total survey design, taking not only sampling but also nonsampling errors into account. A more comprehensive review of historical aspects are provided in Sample Surveys, History of.

3. The Use Of Models

While early developments focused on methods for sample selection in different situations and proper estimation methods, later developments have to a large extent focused on theoretical foundations and the use of probability models for increasing the efficiency of the estimators. There has been a development from implicit modeling to explicit modeling.

The model traditionally used in the early theory is based on the view that what is observed for a unit in the population is basically a fixed value. This approach may be called the ‘fixed population approach.’ The stochastic nature of the estimators is a consequence of the deliberately introduced randomization among the population units. A specific feature of survey sampling is the existence of auxiliary information i.e., known values of a concomitant variable, which is in some sense related to the variable under study, so that it can be used to improve the precision of the estimators. The relationship between the variable under study and the auxiliary variables are often expressed as linear regression models, which often can be interpreted as expressing the belief (common or the sampler’s own) concerning the structure of the relationship between the variables. Such modeling is used extensively in early textbooks, see Cochran 1953. A somewhat different approach is to view the values of the variables as realizations of random variables using probability models. In combination with the randomization of the units this constitutes what is called the superpopulation approach. Model based inference is used to draw conclusions based solely on properties of the probability models ignoring the randomization of the units.

Design based inference on the other hand ignores the mechanism that generated the data and concentrates on the randomization of the units. In general, model-based inference for estimating population parameters like means of subgroups can be very precise if the model is true but may introduce biased estimates if the model is false, while design-based inference leads to unbiased, but possibly inferior estimates of the population parameters. Model assisted inference is a compromise that aims at utilizing models in such a way that, if the model is true the precision is high, but if the model is false the precision will be no worse than if no model had been used.

As an example, we want to study a population of families in a country. We want to analyse the structure of disposable income for the households and find out the relation between factors like age, sex, education, the number of household members, and the disposable income for a family. A possible model of the data generating process could be that the disposable income is a linear function of these background variables. There is also an element of unexplained variation between families having the same values of the background variables. Also, the income will fluctuate from year to year depending on external variation in society. All this shows that the data generating process could be represented by a probability model where the disposable income is a linear function of background variables and random errors over time and between families. The super population model would be the set of models describing how the disposable income is generated for the families. For inferential purposes, a sample of families is selected. Different types of inference can be considered. For instance we might be interested in giving a picture of the actual distribution of the disposable income in the population at the specific time when we selected the sample, or we might be interested in estimating the coefficients of the relational model either because we are genuinely interested in the model itself e.g., for prediction of a future total disposable income for the population, which would be of interest for sociologists, economists and decision makers, or for using the model as a tool for creating more efficient estimators of the fixed distribution, given for example that the distribution of sex and age is known with reasonable accuracy in the population and can be used as auxiliary information. Evidently, the results would depend on the constellation of families comprising our sample. If we use a sample design that over-represents the proportion of large households or young households with small children, compared to the population, the inference based on the sample can be misleading. Model-based inference ignores the sample selection procedure and assumes that the inference conditional on the sample is a good representation of what would have been the case if all families had been surveyed. Design-based inference ignores the data generation process and concentrates on the artificial randomisation induced by the sampling procedure. Model-assisted inference uses models as tools for creating more precise estimates. Broadly speaking, model-based inference is mostly used in the case when the relational model is of primary interest. This is the traditional way of analysing sample data as given in textbooks in statistical theory. Design-based inference on the other hand is the traditional way of treating sample data in survey sampling. It is mainly focused on giving a picture of the present state of the population. Model-assisted inference uses models as tools for selecting estimators, but relies on design properties. It too is mainly focused on picturing the present state in the population.

Modern textbooks such as Cassel et al. (1977) and Sarndal et al. (1992) discuss the foundations of survey sampling and make extensive use of auxiliary information in the survey design. The different approaches mentioned above have their advocates, but most of the surveys conducted around the world still rely heavily on design-based approaches with implicit modeling. But models are needed to take nonsampling errors into account since we do not know exactly how such errors are generated. To make measurement errors part of the inference procedure, one has to make assumptions about the error structures. Such error structures concern cognitive issues, question wording and perception, interviewer effects, recall errors, untruthful answers, coding, editing and so on and so forth. Similarly, to make errors of nonobservation (frame coverage and nonresponse errors) part of the inference procedure, one needs to model the mechanisms that generate these errors. The compromise called model-assisted inference takes advantage of both design and model-based features.

Analysis of data from complex surveys denotes the situation that occurs when the survey statistician is trying to estimate the parameters of a model used for description of a random phenomenon, for example econometric or sociological models such as time series models, regression models or structural equation models. It is assumed that the data available are sample survey data that have been generated by some sampling mechanism that does not support the assumption of independent identically distributed (IID) observations on a random variable. The traditional inference developed for the estimation of parameters of the model (and not for estimating the population parameters) presupposes that IID is at hand. In some cases, traditional inference based on e.g., maximum likelihood gives misleading results. Comprehensive reviews of analysis of data from complex surveys are provided by Skinner et al. (1989) and Lehtonen and Pahkinen (1995).

The present state of affairs is that there is a relatively well-developed sampling theory. The theory of non-sampling errors is still in its infancy, however. A typical scenario is that survey methodologists try to reduce potential errors by using for example, cognitively tested questionnaires and various means to stimulate survey participation, and these things are done to the extent available resources permit. How- ever, not all nonsampling error sources are known and some that are known defy expression. The error reduction strategy can be complemented by sophisticated modeling of error structures. Unfortunately, a rather common implicit modeling seems to be that nonsampling errors have no serious effect on estimates. In some applications, attempts are made to estimate the total error or error components by evaluation techniques, i.e., for a subsample of the units, the survey is replicated using expensive ‘gold standard’ methods and the differences between the preferred measurements and the regular ones are used as estimates of the total errors. This is an expensive and time-consuming procedure that is not very suitable for long-range improvements. A more modern and realistic approach is to develop reliable and predictable (stable) survey processes that can be continuously improved (Morganstein and Marker 1997).

4. Conclusions

Obviously there are a number of future challenges in the field of survey sampling. We will provide just a few examples: (a) Many surveys are conducted in a primitive way because of limited funding and knowhow. The development of more efficient designs taking nonsampling errors into account at the estimation stage is needed. There is also a need for strategies that can help allocate resources to various design stages so that total errors are minimized. Sometimes those in charge of surveys concentrate their efforts on the most visible error sources or where there is a tool available. For instance, most survey sponsors know that nonresponse might be harmful. The indicator of nonresponse error, the nonresponse rate, is both simple and visible. Therefore it might be tempting to put most resources into this error source. On the other hand, not many users are aware of the cognitive phenomena that affect the response delivery mechanism. Perhaps from a total error point of view more resources should be spent on questionnaire design. (b) Modern technology permits simultaneous use of multiple data collection modes within a survey. Multiple modes are used to accommodate respondents, to increase response rates and to allow inexpensive data collection when possible. There are, however, mode effects and there is a need for calibration techniques that can adjust the measurements or the collection instruments so that the mode effect vanishes. (c) International surveys are becoming increasingly important. Most methodological problems mentioned are inflated under such circumstances. Especially interesting is the concept of cultural bias. Cultural bias means that concepts and procedures are not uniformly understood, interpreted, and applied across geographical regions or ethnical subpopulations. To define and measure the impact of such bias is an important challenge.

Bibliography:

• Biemer P, Groves R, Lyberg L, Mathiowetz N, Sudman S 1991 Measurement Errors in Surveys. Wiley, New York
• Bowley A L 1913 Working-class households in reading. Journal of the Royal Statistical Society 76: 672–701
• Bowley A L 1926 Measurement of the precision attained in sampling. Proceedings of the International Statistical Institute XII: 6–62
• Cassel C-M, Sarndal C-E Wretman J 1977 Foundations of Inference in Survey Sampling. Wiley, New York
• Cochran W G 1942 Sampling theory when the sampling-units are of unequal sizes. Journal of the American Statistical Association 37: 199–212
• Cochran W G 1953 Sampling Techniques, 1st edn. Wiley, New York
• Cochran W G 1977 Sampling Techniques, 3rd edn. Wiley, New York
• Couper M, Baker R, Bethlehem J, Clark C, Martin J, Nicholls W, O’Reilly J 1998 Computer Assisted Survey Information Collection. Wiley, New York
• Dalenius T 1957 Sampling in Sweden. Almqvist and Wiksell, Stockholm, Sweden
• Dalenius T 1985 Elements of Survey Sampling. Notes prepared for the Swedish Agency for Research Cooperation with Developing Countries (SAREC)
• DeLeeuw E, Collins M 1997 Data collection methods and survey quality: An overview. In: Lyberg L, Biemer P, Collins M, DeLeeuw E, Dippo C, Scwarz N, Trewin D (eds.) Survey Measurement and Process Quality. Wiley, New York
• Dillman D 2000 Mail and Internet Surveys: The Tailored Design Method, 2nd edn, Wiley, New York
• Groves R 1989 Survey Errors and Survey Costs. Wiley, New York
• Groves R, Biemer P, Lyberg L, Massey J, Waksberg J (eds.) 1988 Telephone Survey Methodology. Wiley, New York
• Hansen M H, Hurwitz W N 1943 On the theory of sampling from finite populations. Annals of Mathematical Statistics 14: 333–62
• Hansen M H, Hurwitz W N 1949 On the determination of optimum probabilities in sampling. Annals of Mathematical Statistics 20: 426–32
• Hansen M H, Hurwitz W N, Madow W G 1953 Sample Survey Methods and Theory (I and II). Wiley, New York
• Hartley H O 1974 Multiple methodology and selected applications. Sankhya, Series C 36: 99–118
• Kiaer A N 1897 The representative method for statistical surveys (original in Norwegian). Kristiania Videnskabsselskabets Skrifter. Historisk-filosofiske klasse 4: 37–56
• Lehtonen R, Pahkinen E J 1995 Practical Methods for Design and Analysis of Complex Surveys. Wiley, New York
• Lyberg L, Biemer P, Collins M, DeLeeuw E, Dippo, C, Schwarz N, Trewin D (eds.) 1997 Survey Measurement and Process Quality. Wiley, New York
• Lyberg L, Kasprzyk D 1991 Data collection methods and measurement error: An overview. In: Biemer P, Groves R, Lyberg L, Mathiowetz N, Sudman S (eds.) Measurement Errors in Surveys. Wiley, New York
• Madow W G, Madow L H 1944 On the theory of systematic sampling, I. Annals of Mathematical Statistics 15: 1–24
• Mahalanobis P C 1946 On large-scale sample surveys. Philosophical Transactions of the Royal Society London, Series B 231: 329–451
• Morganstein D, Marker D 1997 Continuous Quality Improvement in Statistical Agencies. In Lyberg L, Biemer P, Collins M, DeLeeuw E, Dippo C, Scwarz N, Trewin D (eds.) Survey Measurement and Process Quality. Wiley, New York
• Neyman J 1934 On the two different aspects of the representative method: The method of stratified sampling and the method of purposive selection. Journal of the Royal Statistical Society 97: 558–625
• Neyman J 1938 Contribution to the theory of sampling human populations. Journal of the American Statistical Association 33: 101–16
• Sarndal C-E, Swensson B, Wretman J 1992 Model Assisted Survey Sampling. Springer-Verlag, New York
• Skinner C, Holt D, Smith T M F (eds.) 1989 Analysis of Complex Surveys. Wiley, New York
• Sudman S, Bradburn N, Schwarz N 1996 Thinking About Answers: The Application of Cognitive Processes to Survey Methodology. Jossey-Bass, San Francisco, CA
• Tanur J (ed.) 1992 Questions About Questions. Russell Sage, New York
• Tschuprow A A 1923 On the mathematical expectation of the moments of frequency distributions in the case of correlated observation. Metron 2: 461–93; 646–80

ORDER HIGH QUALITY CUSTOM PAPER

Population-level norm values by EQ-5D-3L in Hungary - a comparison of survey results from 2022 with those from 2000

• Open access
• Published: 05 June 2024

Cite this article

You have full access to this open access article

• András Inotai   ORCID: orcid.org/0000-0002-0663-2733 1 ,
• Dávid Nagy 1 , 2 ,
• Zoltán Kaló 1 , 2 &
• Zoltán Vokó 1 , 2  

Although population norms of the EQ-5D-3L instrument had been available in Hungary since 2000, their evaluation was based on a United Kingdom (UK) value set. Our objective was to estimate the population norms for EQ-5D-3L by using the new Hungarian value set available since 2020, to extend the scope to adolescents, and to compare with norms from 2000.

A cross sectional EQ-5D-3L survey representative of the Hungarian population was conducted in 2022. The EQ-5D-3L dimensional responses were analyzed by age and sex and compared with the survey from 2000, by estimating population frequencies with their 95% confidence intervals; index values were evaluated by both value sets.

Altogether, 11,910 respondents, aged 12 or more (578 between 12 and 17), completed the EQ-5D-3L. There was a notable improvement in reporting problems for both sexes (age 35–64) regarding the pain/discomfort and anxiety/depression compared to 2000. Below the age 44, both sexes had an EQ-5D-3L index plateau of 0.98, while above the age 55, men tended to have numerically higher index values compared to women, with the difference increasing with older age. Improvement in dimensional responses were also translated to numerically higher index values for both sexes between ages 18 and 74 compared to 2000. Multivariate regression analysis showed that higher educational attainment, lower age, larger household size, and active occupational status were associated with higher index values.

Over the past 22 years, there was a large improvement in HRQoL of the middle-aged to elderly men and women in Hungary.

Plain English Summary

Health states can be described by a combination of statements of health-related quality of life measures. ‘Value sets’ are numerical expressions of how preferred a health state is. The provision of population-level health-related quality of life estimates (also known as ‘population norms’) are expected to improve the precision of patient-level clinical decision making, and health economic and public health studies. However, preference towards these health states is influenced by culture, resulting in differences across populations. While responses for the EQ-5D-3L instrument for adults have been available in Hungary since 2000, the evaluation of these responses was based on a ‘value set’ from the United Kingdom, rather than a Hungarian one.

This research, utilizing the newly introduced Hungarian ‘value set’ (available since 2020) for the EQ-5D-3L instrument, offers a larger sample size, inclusion of adolescents and potentially improved sampling compared to the prior research conducted in 2000. Comparison of the two surveys allows us to estimate changes in both dimensional responses and overall health-related quality of life of the population over a 20-year time horizon, while we also compare the impact of different ‘value sets’ on health-related quality of life assessment. A large EQ-5D-3L improvement was observed in middle-aged-to-elderly people.

Avoid common mistakes on your manuscript.

Introduction

Health-related quality of life (HRQoL) is a subjective multi-dimensional concept that includes dimensions related to physical, mental, emotional, and social functioning and that goes beyond clinical measures of health status [ 1 ]. Standardized and validated generic and disease-specific measures are used to estimate the HRQoL of individuals [ 2 ]. Generic measures are universally applicable in a wide range of diseases.

One of the most widely used generic HRQoL instrument is the EQ-5D developed by the EuroQoL Group. All EQ-5D questionnaires include a descriptive system focusing on five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression, and a vertical visual analogue scale for self-assessment of health status (EQ VAS). EQ-5D-3L and −5L are popular generic measures among adults, and EQ-5D-Y-3L and −5L are targeted for the younger population with a child-friendly wording [ 3 ]. In the EQ-5D-3L index, each dimension has three levels (3L), with level 1 (L1) denoting no problems and level 3 (L3) denoting ‘extreme problems/unable to/confined to bed’. (In the 5L version, each dimension has five levels). The EQ VAS (also known as EQ-5D Thermometer) is ranged from 0 to 100 (‘the best and worst health you can imagine’) [ 4 , 5 ].

EQ-5D instruments’ patient-reported values (profile scores) can be converted to an index score using a selected algorithm. Such algorithms are based on surveying the general public’s preferences for different combinations of health states, resulting in ‘value sets’ that are numerical expressions of how preferred a health state is. Therefore, measures such as EQ-5D are also referred to as a ‘preference-based’ or ‘preference-accompanied measure’. The measurement interval of the value set developed originally for EQ-5D-3L by Dolan (using the time trade-off (TTO) method, based on a United Kingdom (UK) population sample) is between -0.594 (health state 33333) to 1 (health state 11111), where the value of 0.0 refers to being dead, 1.0 refers to full health (‘UK value set’) [ 6 , 7 , 8 , 9 ].

However, as individuals in different cultures or countries may assign different values to certain health states, many countries other than the UK have also developed their own value sets since the introduction of the EQ-5D-3L [ 10 ]. Since 2020, a Hungarian value set has also been available for both the EQ-5D-3L and the 5L, based on a non-probability quota sample of 1000 respondents in the Hungarian general population, by using a composite TTO method [ 11 ] (‘Hungarian (HU) value set’). Quotas are set by age and sex. Index values in the Hungarian value set range from -0.865 to 1 for the EQ-5D-3L health states. EQ-5D is also among the preferred instruments for health technology assessment (HTA) in Hungary [ 12 ].

Population norm values are used as a reference to estimate the HRQoL decrement of a patient population with different diseases. They are applied both in micro-level clinical decision-making (estimating HRQoL) and in health economic models, and macro-level public health decisions (estimating health loss/burden of disease [ 13 ]). In Hungary, the poor general health status of the population has led to several population-level health surveys by using the EQ-5D-3L instrument. Most importantly, in 2000, a national health survey was conducted on a representative sample of 5503 individuals representing the whole population, age and sex, by using a paper-based self-administered EQ-5D-3L [ 14 , 15 ]. This was followed by the European Value of a Quality Adjusted Life Year (EuroVaQ) project in 2010, in which a population sample of 2281 individuals completed a web-based EQ-5D-3L (although it was intended to be representative, the authors reported that the sample overrepresented women and underrepresented the elderly population) [ 16 ]. Both surveys applied the UK value set and used an adult sample. The publication of the Hungarian value set in 2020 provided a solid platform for re-estimating the index values of the EQ-5D-3L for the Hungarian adult population. In parallel, the increasing number of HTA submission dossiers with pediatric/adolescent indication also necessitated defining population norm values for the under-18 population to have a more accurate estimate of their cost effectiveness in the Hungarian setting. This led to the need for population health surveys covering an extended age range, also including the 12–17 age band.

This paper aims to present an overview of the HRQoL of the Hungarian population aged 12 years and older based on a representative random sample using the EQ-5D-3L instrument, and to compare the results with the prior survey from 2000.

This survey was part of a larger cross-sectional national survey on travelling habits of Hungarian people, conducted by the Hungarian Central Statistical Office on a quarterly basis. EQ-5D-3L was an add-on to this survey using standard demographic questions, but without any other health care-related questions.

Selection of instrument and age

Although the EQ-5D-5L was also considered for the study, the EQ-5D-3L was ultimately selected. This allows for a comparison with prior population surveys [ 14 , 15 , 16 ], with several existing disease-specific HRQoL studies using EQ-5D-3L (conducted both at Semmelweis University [e.g. 17 – 22 ] and at other research centers in Hungary [e.g. 23 – 28 ]), and also with high quality validation studies [ 29 , 30 , 31 , 32 ]. As the EuroQoL Group recommends the EQ-5D-3L adult version for adolescents aged 16 and above, and considers both the EQ-5D-3L adult version and the EQ-5D-Y-3L to be acceptable for adolescents aged 12–15 years, for logistical simplicity (i.e., using only one instrument) and in concordance with EQ-5D-Y-3L user guide, the minimum eligible age was 12 years in this study, while the EQ-5D-3L version was applied also for adolescents aged 12–17 years. As health economic models benefit from more precise index value data and also to minimize residual confounding by age, index values are reported per a 5-year age band. However, to ensure comparability with the national health survey from 2000, also a 10-year age band was applied in this study.

Research ethics

The study protocol was approved by the Medical Research Council – Scientific and Ethical Committee in Hungary (number of ethical approval: IV/2292-1 2022/EKU), and research was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki [ 33 ].

Population survey sampling

Primary sampling units (PSUs) were settlements, and the secondary sampling units were dwellings in Hungary. The settlements were stratified by county and size. Larger settlements were selected with certainty. No general national threshold was applied to define certainty PSUs, it varied from county to county. The probability of selecting smaller PSUs was proportional to their size in terms of dwellings. Dwellings were randomly selected within a settlement. All household members aged 12 years or older were included in the survey.

Population survey weighting

Design weights were calculated based on the sampling design. After the data collection, these were calibrated to correct for nonresponse by geographical region to population size, sex and age distribution, development category of the settlement, and household size, so that we could provide unbiased estimates on the level of the population. These analytical weights were used in the statistical analysis. They had a range of 140–2500, reflecting the number of people a study participant represented. To include 11,910 respondents aged 12 or more, 15,058 individuals were contacted in 7578 households, resulting in a response rate of 79%. Of all the EQ-5D-3L questionnaires, 2.81% were self-administered online, 46.64% by telephone interview, and 50.55% by personal interview, between 1st April and 2nd May 2022. The questionnaire was designed in such a way that it did not allow item nonresponse, ‘I do not know/I do not respond’ answers. As the questionnaire was short, withdrawing the participation in the meantime did not happen. No deletion of responders due to lack of data or imputation was necessary. The participant (unit)-level non-response was corrected for in the weighting, the description of which was provided earlier.

Statistical analysis methods

We used the survey module of the statistical software STATA 16.1 [ 34 ]. Dimensional distribution analysis was performed by estimating population frequencies with their 95% confidence intervals, by using the same age bands and reporting structure as applied in the 2000 national health survey. Mean EQ-5D-3L index values with their 95% confidence intervals were estimated for the target population by age and sex using “svy: mean” procedure with an analytically derived variance estimator associated with the sample mean. Weighted multiple linear regression analyses were applied by using sex, age (adults only), education, occupation, and household size as explanatory variables applying “svy: regress” procedure. Additionally, we fitted a regression model with the interaction terms between sex and age bands adjusted for education, occupation, and household size. Design-based standard errors were estimated taking into account the stratified cluster sampling.

Sample characteristics

Table  1 describes the baseline characteristics of the sample including age, sex, geographical region, education, occupation and household size, also by the mode of administration. In the unweighted sample 54.3% of participants were women, 33.8% were students and 11.5% participants were from Budapest. Online responders tend to be younger and living in Budapest with a higher educational attainment.

EQ-5D-3L dimensional, index (using HU value set), and VAS norms by age and sex

Supplementary Table 1 reports weighted EQ-5D-3L questionnaire responses by mode of administration. Problems (L2 + L3) in anxiety/depression were reported slightly more frequently in the case of online self-administration, compared to the telephone and online interviews. Supplementary Table 2 reports the weighted EQ-5D-3L dimensional responses for 5- and 10-year age bands. Among the five dimensions, anxiety/depression was the one where both sexes reported problems (L2 + L3) even in the younger age bands. Towards higher age bands, mobility, pain/discomfort and usual activities were increasingly associated with problems. Similarly, L3 impairments were reported mainly in the dimensions of usual activities and pain/discomfort by older adults. Comparing the two sexes, younger men tended to report slightly more problems in all dimensions; on contrary, above 75 women tended to report more problems. Along with older ages, women tended to report more L3 impairments in the dimension of pain/discomfort, while men tended to report more L3 impairments in self-care, compared to the other sex.

Table  2 reports the weighted EQ-5D-3L index values from the 2022 population survey (evaluated by using the Hungarian value set) of the 12-year-old and older by 5- and 10-year age bands. Using the Hungarian value set, index values showed a plateau of 0.98 under age of 45. Men older than 54 years generally had numerically higher, although statistically not significantly different index values compared to women, with the difference slightly increasing with older ages. In contrast to adults, the index values for girls were minimally higher than for boys among participants under 18 years of age.

Table  2 also reports EQ VAS data in a similar age structure. Above the age of 34, men tended to have minimally higher EQ VAS compared to women in every 10-year age bands. Above age 39, EQ VAS tended to decrease in every consecutive 5-year age band in both sexes.

Comparison of the dimensional responses between 2000 and 2022

Supplementary Table 3 reports comparison of problems by dimension between 2000 and 2022, using the data structure of the 2000 national survey. There was a notable improvement in reporting problems (L2 + L3) for both sexes in the age band of 35–64 in all dimensions except for self-care, with a major improvement in both pain/discomfort and anxiety/depression, especially for women. Some improvement was also seen in pain/discomfort (for age 18–34) and anxiety/depression (for both age 18–34 and 65+) from 2000 to 2022 for both sexes, but a numerically larger one for women.

Comparison of index values (using the UK value set) between 2022 and 2000

Figure  1 shows a comparison of weighted EQ-5D-3L index values of the 2022 population survey with the 2000 national health survey for 10-year age bands, both using the UK value set to ensure consistency. Supplementary Table 4 reports the weighted EQ-5D-3L index values for the same comparison for 10-year age bands. The 2022 population survey resulted in higher index values than the 2000 national health survey between age 18–74, especially between the ages 35 and 64, where better dimensional responses of the 2022 population survey were also translated into higher weighted index values for both sexes compared to the 2000 national health survey. On contrary, for the 85+ age band, the 2022 population survey showed markedly lower weighted index values compared to the 2000 national health survey. In that study, the difference by sex was numerically even larger in all relevant age bands.

Comparison of EQ-5D-3L index values (using the UK value set) in both the 2022 population survey and the 2000 national health survey

Comparison of index values derived by the HU and UK value sets for the 2022 population survey

As an overview, Fig.  2 presents the weighted EQ-5D-3L index data for both sexes from the 2022 population survey by using both the Hungarian (default) and the UK value sets for 10-year age bands. Index values derived by the UK value set from the 2022 population survey (see Supplementary Table 4 ) showed a similar trend to those indices derived by the HU value set from 2022 (see Table  2 ), but with lower weighted index values above age of 45 and a larger, statistically not significant difference between men and women, especially in older age groups.

EQ-5D-3L index values (using the UK and Hungarian value set) per 10-year age bands and sex in 2022 population survey

Comparison of VAS results

Supplementary Table 5 reports the weighted EQ VAS numbers for the same comparison for 10-year age bands. EQ VAS numbers were consistently higher in both sexes in 2022 compared to 2000 for with the smallest difference for those aged 85+.

Regression results

Finally, Table  3 presents the results of the weighted multiple linear regression analysis estimating the EQ-5D-3L index values for adults. Multivariate analysis showed that, after controlling for other variables, sex had no significant impact on the index values, but there was a significant interaction between sex and age (adjusted Wald-test p -value: 0.014). In terms of age, the index values significantly differ from the index value of the reference age band (18–24 years) above the age of 49 years. Higher educational attainment was associated with higher index values. Economic activity was used as a nominal variable in the model and showed a significant impact on the index values: employed respondents and students had significantly higher index values than those who were unemployed, retired or inactive for other reasons, the latter having the lowest index values. Finally, a larger household size (a larger number of people living in a household) was also associated with higher index values.

Our results showed that in 2022 among the five dimensions, anxiety/depression was the one where both sexes reported problems in younger age bands; towards higher age bands, mobility, pain/discomfort and usual activities were increasingly associated with problems. Both EQ-5D-3L index values and EQ VAS showed reduction along with age above the age of 44 in both sexes, with men having somewhat higher values compared to women. EQ VAS numbers were consistently higher in 2022 compared to 2000 for both sexes, and there was a large improvement in EQ-5D-3L index values between age 35–64. According to the multivariable analysis, younger age, higher education, being active, and larger household size are associated with better HRQoL.

Comparison with the national health survey 2000

Over the past 22 years the HRQoL of women aged 25–74 and men aged 35–64 improved considerably. Interestingly, our study could not replicate the relatively high index values observed in the 2000 national health survey for the age 85+ (for both men and women). However, in that survey, only 1% of respondents had this age which meant that some of the outliers may have had a potentially larger impact. Since 2000, the health status of elderly people has improved (life expectancy at age 65 improved from 17.3 to 18.7 for women and 13.4 to 14.6 for men between 2004 and 2016) [ 35 ], which (if better HRQoL is also assumed) may contradict our results showing lower index values. It seems that the elderly population with a better HRQoL may have been overrepresented in the 2000 national health survey, and the small number of participants of this age provided less robust estimates for this age band.

Overall, beyond using a more relevant value set for EQ-5D-3L, our study also offers larger sample size, thus enhanced statistical power, narrow age bands for more precise economic evaluations, inclusion of adolescents and potentially improved sampling, compared to the 2000 national health survey. We strongly believe that these factors contribute to more credible population norm estimates.

Impact of UK and HU value sets on index values from the 2022 population survey

Higher index values derived using the Hungarian value set compared to the UK value set (shown in Fig.  2 ) can be explained by two key factors. Firstly, the Hungarian value set uses a 0.020 constant (decrement, to be used for health states other than 11111) instead of 0.081 used by UK value set. Secondly and more importantly, the new Hungarian value set does not apply the constant N3 (an additional  -0.269 decrement for L3 responses in any dimension used by UK value set, beyond the respective dimension-specific L3 decrement), as its impact has been considered in larger L3 decrements in the Hungarian value set compared to the UK one. On the other hand, for L2 responses which were reported much more frequently (Supplementary Table 2 ), the new Hungarian value set tend use smaller decrements compared to the UK one.

Comparison with other population norms in the region

Nikl et al. published population norms for Hungarian population on a sample of 2000 adults, reported to be broadly representative in terms of sex, age groups, highest level of education, geographical region, and settlement type, using the 15D instrument [ 36 ]. The mean 15D index value was 0.810 using the Norwegian 15D value set. In that study, with advancing age categories, the 15D index values showed an inverse U-shaped curve with highest index values of 0.82 for both age bands of 25–34 and 45–54; and numerical results could be considered somewhat consistent with index values (derived by the UK value set) of this research. However, different HRQoL instruments, sample sizes, value sets, recruitment (i.e., voluntary registration from online panel) make the more detailed comparison of the two studies difficult. In Poland, Golicki and Niewada published population norms on a sample of 3963 adults, representative of the Polish population in terms of age, sex, geographical region, education, and socio-professional group, by using a self-administered EQ-5D-5L instrument [ 37 ]. To calculate index values, an interim EQ-5D-5L value set for Poland was used based on a crosswalk methodology. Index values (0.96 for those aged 18–24, 0.94 for 35–44, 0.9 for 45–54 and 0.81 for 65–74, respectively) were broadly consistent yet somewhat lower than the Hungarian ones (derived by the Hungarian value set). Again, differences in the applied instruments, sample sizes and administration make further direct comparison of the norms between the two countries difficult. In both studies, similarly to our results, men tend to have higher values in almost all age bands, especially above 35 years. Finally, Zrubka et al. compared EQ-5D-3L population studies from Hungary, Slovenia and Poland and reported issues in terms of comparability due different national characteristics, different data collection methodologies and times [ 38 ]. Importantly, data gaps for age 65+ were reported to be a general concern, confirming our findings in the oldest age category in the 2000 national health survey.

Regression analyses: younger age, higher education, being active and having a larger household size were associated with better HRQoL

To minimize the impact of adult value set applied also for adolescents, our regression analysis included adults only. The lower EQ-5D-3L index in elderly individuals may be explained by the fact that elderly people tend to suffer from more diseases, including multi-morbid conditions [ 39 ], which potentially have a major impact on HRQoL. Better education is shown to be associated with healthier lifestyles [ 40 , 41 , 42 ], higher participation in prevention programs, and appreciation of being healthy in general. Active occupation (i.e., employee, student) may lead to more physical activities and/or social contact, which may contribute to higher index values. On the contrary, inactive people may lack these, while those involved in childcare may feel isolated, sleepless or experience maternal depression, potentially associated with poorer HRQoL. Finally, interpersonal relationships are likely to be stronger in households with two or more people. Moreover, in larger families, the average household index value seems to be increasing significantly with household size even after controlling for the other factors listed in Table  3 . Our findings on the association of age and education with HRQoL were also confirmed by similar conclusions from the 2000 national health survey [ 14 ] and the EuroVaQ study [ 16 ]. Interestingly, these prior studies also found that sex had a significant impact on the EQ-5D-3L index, as did household income (however, this latter variable was not included in the 2022 population survey). This is in line with our observation that there was an interaction between sex and age in our study.

Implications and future research

This research has significant policy implications. The new population norm values for those under 18 introduced by this research will have a significant impact both on the accuracy of health burden estimates and the economic evaluation of health technologies for adolescents. Similarly, the application of the new Hungarian value set that truly reflects the preferences of Hungarian people for different health states are expected to improve the accuracy of health burden estimates and economic evaluations for adults. Moreover, as a Hungarian value set for the EQ-5D-Y-3L has also been available since 2022 [ 43 ], future research could compare the impact of using the EQ-3D-3L (with the adult value set) and the future use of the EQ-5D-Y-3L (with the new value set for adolescents) to conclude on the applicability of the adult EQ-5D-3L for adolescents aged 12–15 years, as considered to be acceptable by the EuroQoL Group [ 44 ]. Finally, this study together with the research conducted in 2000, with some limitations (difference in sampling methodology, mixing EQ-5D-3L administration methods, various factors influencing population change over 20 years etc.), allows researchers to estimate changes in HRQoL of the population over a 20-year time horizon.

Strengths and limitations

The large representative random sample and the wide age range are the main strengths of this study. Compared to some other large-scale surveys, our study had more robust outreach for those above 65, a cohort especially relevant from public health and health economic point-of-view. However, it has some limitations, too. First, as the intention with the population-level health survey was to establish norm values for previous and ongoing disease-specific research in Hungary, and also to ensure comparability with the 2000 national health survey, the EQ-5D-5L was not considered for this research. Second, different administration methods of the questionnaire may have introduced bias even in a homogeneous sample. Third, the analytical weights had a relatively large range and, as noted by Potter and Zeng [ 45 ], ‘extreme variation in the sampling weights can result in excessively large sampling variances when the data and the selection probabilities are not positively correlated’. Finally, using the adult EQ-5D-3L also for 12-15-year-old adolescents may also have had an impact on the results.

Over the past 22 years, there was a large improvement in reporting problems for both sexes (especially for women) in age 35–64 in EQ-5D-3L dimensions of pain/discomfort and anxiety/depression, compared to 2000. This was also translated to considerably higher index values for middle-aged women and men. Younger age, higher education, being active, and larger household size are associated with better HRQoL. The study, using the new national value set and extended age, is expected to improve the accuracy of economic evaluations and disease burden studies in Hungary.

Data availability

All data generated or analyzed during this study are available from the authors upon reasonable request and with permission of the Hungarian Central Statistical Office.

Office of Disease Prevention and Health Promotion (2020). Health-Related Quality of Life and Well-Being. Health-Related Quality of Life and Well-Being | Healthy People 2020. Retrieved October 13, 2022, from: https://www.healthypeople.gov/2020/about/foundation-health-measures/Health-Related-Quality-of-Life-and-Well-Being

Guyatt, G. H., Feeny, D. H., & Patrick, D. L. (1993). Measuring health-related quality of life. Annals of Internal Medicine , 118 (8), 622–629. https://doi.org/10.7326/0003-4819-118-8-199304150-00009

Article   CAS   PubMed   Google Scholar  

EuroQoL Group (2022). EuroQoL instruments. Retrieved October 13, 2022, from: https://euroqol.org/

Brooks, R. (1996). EuroQoL: The current state of play. Health Policy , 37 (1), 53–72. https://doi.org/10.1016/0168-8510(96)00822-6

EuroQoL Group. (1990). EuroQoL–a new facility for the measurement of health-related quality of life. Health Policy , 16 (3), 199–208. https://doi.org/10.1016/0168-8510(90)90421-9

Article   Google Scholar  

Dolan, P. (1997). Modeling valuations for EuroQoL health states. Medical Care , 35 , 1095–1108.

Lugnér, A. K., & Krabbe, P. F. M. (2020). An overview of the time trade-off method: Concept, foundation, and the evaluation of distorting factors in putting a value on health. Expert Rev Pharmacoecon Outcomes Res , 20 (4), 331–342. https://doi.org/10.1080/14737167.2020.1779062

Article   PubMed   Google Scholar  

Drummond, M. F., O’Brein, B., Stoddart, G. L., & Torrance, G. W. (1997). Methods for economic evaluations of healthcare programmes . Oxford University Press.

Berger, M. L., Bingefors, K., Hedblom, E. C., Pashos, C. L., & Torrance, G. W. (2003). Health care cost, quality and outcomes – ISPOR book of terms . ISPOR.

Roudijk, B., Donders, A. R. T., Stalmeier, P. F. M., Cultural, & Values Group. (2019). Cultural values: Can they explain differences in Health utilities between countries? Medical Decision Making , 39 (5), 605–616. https://doi.org/10.1177/0272989X19841587

Article   PubMed   PubMed Central   Google Scholar  

Rencz, F., Brodszky, V., Gulácsi, L., Golicki, D., Ruzsa, G., Pickard, A. S., et al. (2020). Parallel valuation of the EQ-5D-3L and EQ-5D-5L by Time Trade-Off in Hungary. Value In Health : The Journal of the International Society for Pharmacoeconomics and Outcomes Research , 23 (9), 1235–1245. https://doi.org/10.1016/j.jval.2020.03.019

Rencz, F., Gulácsi, L., Drummond, M., Golicki, D., Rupel, P., Simon, V., J., et al. (2016). EQ-5D in Central and Eastern Europe: 2000–2015. Quality of Life Research , 25 (11), 2693–2710. https://doi.org/10.1007/s11136-016-1375-6

Inotai, A., Ágh, T., & Mészáros, Á. (2012). Quality of life, utility and health burden in asthma, chronic obstructive pulmonary disease and rheumatoid arthritis. International Journal of Person Centered Medicine , 2 , 505–510.

Google Scholar  

Szende, A., & Nemeth, R. (2003). Health-related quality of life of the Hungarian population. Orvosi Hetilap , 144 (34), 1667–1674.

PubMed   Google Scholar  

Boros, J., Németh, R., & Vitrai, J. (2002). National Health Survey 2000. [In Hungarian] Retrieved October 13, 2022, from: https://www.nnk.gov.hu/attachments/article/846/Kutatasi_jelentes2000.pdf

Baji, P., Brodszky, V., Rencz, F., Boncz, I., Gulácsi, L., & Péntek, M. (2015). Health state of the Hungarian population between 2000–2010. Orv Hetil , 156 (50), 2035–2044. [In Hungarian.].

Szekeres, G., Rozsa, S., Dome, P., Barsony, G., & Gonda, X. (2021). A Real-World, prospective, Multicenter, single-arm observational study of Duloxetine in patients with major depressive disorder or generalized anxiety disorder. Frontiers in Psychiatry , 12 , 689143. https://doi.org/10.3389/fpsyt.2021.689143

Rieckmann, N., Neumann, K., Feger, S., Ibes, P., Napp, A., Preuß, D., et al. (2020). Health-related qualify of life, angina type and coronary artery disease in patients with stable chest pain. Health and Quality of Life Outcomes , 18 (1), 140. https://doi.org/10.1186/s12955-020-01312-4 . Erratum in: Health Qual Life Outcomes, 18(1), 205.

Rencz, F., Gulácsi, L., Péntek, M., Poór, A. K., Sárdy, M., Holló, P., et al. (2018). Proposal of a new scoring formula for the Dermatology Life Quality Index in psoriasis. British Journal of Dermatology , 179 (5), 1102–1108. https://doi.org/10.1111/bjd.16927

Hankó, B., Kázmér, M., Kumli, P., Hrágyel, Z., Samu, A., Vincze, Z., et al. (2007). Self-reported medication and lifestyle adherence in Hungarian patients with type 2 diabetes. Pharmacy World & Science , 29 (2), 58–66. https://doi.org/10.1007/s11096-006-9070-2

Inotai, A., Rojkovich, B., Fülöp, A., Jászay, E., Agh, T., & Mészáros, A. (2012). Health-related quality of life and utility in patients receiving biological and non-biological treatments in rheumatoid arthritis. Rheumatology International , 32 (4), 963–969. https://doi.org/10.1007/s00296-010-1721-x

Ágh, T., Inotai, A., & Mészáros, Á. (2011). Factors associated with medication adherence in patients with chronic obstructive pulmonary disease. Respiration , 82 (4), 328–334. https://doi.org/10.1159/000324453

Fekete, H., Guillemin, F., Pallagi, E., Fekete, R., Lippai, Z., Luterán, F., et al. (2020). Evaluation of osteoarthritis knee and hip quality of life (OAKHQoL): Adaptation and validation of the questionnaire in the Hungarian population. Ther Adv Musculoskelet Dis , 12 , 1759720X20959570. https://doi.org/10.1177/1759720X20959570

Péntek, M., Gulácsi, L., Herszényi, L., Banai, J., Palatka, K., Lakatos, P. L., et al. (2021). Subjective expectations regarding longevity and future health: A cross-sectional survey among patients with Crohn’s disease. Colorectal Disease , 23 (1), 105–113. https://doi.org/10.1111/codi.15357

Farkas, K., Kolossváry, E., & Járai, Z. (2020). Simple assessment of quality of life and lower limb functional capacity during cilostazol treatment – results of the Short-tERm cIlostazol eFFicacy and quality of life (SHERIFF) study. Vasa , 49 (3), 235–242. https://doi.org/10.1024/0301-1526/a000845

Brodszky, V., Péntek, M., Bálint, P. V., Géher, P., Hajdu, O., Hodinka, L., et al. (2010). Comparison of the psoriatic arthritis quality of life (PsAQoL) questionnaire, the functional status (HAQ) and utility (EQ-5D) measures in psoriatic arthritis: Results from a cross-sectional survey. Scandinavian Journal of Rheumatology , 39 (4), 303–309. https://doi.org/10.3109/03009740903468982

Vokó, Z., Németh, R., Nagyjánosi, L., Jermendy, G., Winkler, G., Hídvégi, T., et al. (2014). Mapping the Nottingham Health Profile onto the preference-based EuroQoL-5D instrument for patients with diabetes. Value Health Reg Issues , 4 , 31–36. https://doi.org/10.1016/j.vhri.2014.06.002

López-Bastida, J., Linertová, R., Oliva-Moreno, J., Serrano-Aguilar, P., Posada-de-la-Paz, M., Kanavos, P., et al. (2016). Social/economic costs and health-related quality of life in patients with scleroderma in Europe. The European Journal of Health Economics , 17 (Suppl 1), 109–117. https://doi.org/10.1007/s10198-016-0789-y

Koszorú, K., Hajdu, K., Brodszky, V., Bató, A., Gergely, L. H., Kovács, A., et al. (2023). Comparing the psychometric properties of the EQ-5D-3L and EQ-5D-5L descriptive systems and utilities in atopic dermatitis. The European Journal of Health Economics , 24 (1), 139–152. https://doi.org/10.1007/s10198-022-01460-y

Bató, A., Brodszky, V., Gergely, L. H., Gáspár, K., Wikonkál, N., Kinyó, Á., et al. (2021). The measurement performance of the EQ-5D-5L versus EQ-5D-3L in patients with hidradenitis suppurativa. Quality of Life Research , 30 (5), 1477–1490. https://doi.org/10.1007/s11136-020-02732-x

Poór, A. K., Rencz, F., Brodszky, V., Gulácsi, L., Beretzky, Z., Hidvégi, B., et al. (2017). Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L in psoriasis patients. Quality of Life Research , 26 (12), 3409–3419. https://doi.org/10.1007/s11136-017-1699-x

Rencz, F., Lakatos, P. L., Gulácsi, L., Brodszky, V., Kürti, Z., Lovas, S., et al. (2019). Validity of the EQ-5D-5L and EQ-5D-3L in patients with Crohn’s disease. Quality of Life Research , 28 (1), 141–152. https://doi.org/10.1007/s11136-018-2003-4

World Medical Association (2013). Declaration Of Helsinki – Ethical Principles For Medical Research Involving Human Subjects. Retrieved November 30, 2022, from: https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/

StataCorp (2019). Stata Statistical Software: Release 16. College Station . StataCorp LLC.

Hungarian Central Statistical Office (2016). Healthy life expectancy at age of 65, by gender (2004-16). [in Hungarian] Retrieved, November 30, 2022, from: https://www.ksh.hu/docs/hun/eurostat_tablak/tabl/tsdph220.html

Nikl, A., Janssen, M. F., Brodszky, V., & Rencz, F. (2023). Hungarian population norms for the 15D generic preference-accompanied health status measure. Quality of Life Research , 14 . https://doi.org/10.1007/s11136-023-03514-x

Golicki, D., & Niewada, M. (2017). EQ-5D-5L Polish population norms. Arch Med Sci, 1;13(1):191–200. https://doi.org/10.5114/aoms.2015.52126

Zrubka, Z., Golicki, D., Prevolnik-Rupel, V., Baji, P., Rencz, F., Brodszky, V., et al. (2019). Towards a central-eastern European EQ-5D-3L population norm: Comparing data from Hungarian, Polish and Slovenian population studies. The European Journal of Health Economics , 20 (Suppl 1), 141–154. https://doi.org/10.1007/s10198-019-01071-0

Holzer, B. M., Siebenhuener, K., Bopp, M., & Minder, C. E. (2017). Evidence-based design recommendations for prevalence studies on multimorbidity: Improving comparability of estimates. Popul Health Metr , 15 (1), 9. https://doi.org/10.1186/s12963-017-0126-4

Hungarian Central Statistical Office (2019). Physical activities. [in Hungarian] Retrieved November 30, 2022, from: https://www.ksh.hu/docs/hun/xftp/idoszaki/elef/testmozgas_2019/testmozgas_2019.pdf

Hungarian Central Statistical Office (2019). Smoking habits. [in Hungarian] Retrieved November 30, 2022, from: https://www.ksh.hu/docs/hun/xftp/idoszaki/elef/dohanyzas_2019/dohanyzas_2019.pdf

Hungarian Central Statistical Office (2019). Health overview. [in Hungarian]. Retrieved November 30, 2022, from: https://www.ksh.hu/docs/hun/xftp/idoszaki/pdf/egeszsegugyi_helyzetkep_2019.pdf

Rencz, F., Ruzsa, G., Bató, A., Yang, Z., Finch, A. P., & Brodszky, V. (2022). Value Set for the EQ-5D-Y-3L in Hungary. Pharmacoeconomics , 20 , 1–11. https://doi.org/10.1007/s40273-022-01190-2

EuroQoL Group (2020). EQ-5D-Y User Guide. Recommended age range of users of the EQ-5D-Y version. Retrieved October 13, 2022, from: https://euroqol.org/publications/user-guides/

Potter, F., & Zeng, Y. (2015). Methods and Issues in Trimming Extreme Weights in Sample Surveys. In: Proceedings of the Joint Statistical Meetings 2015 Survey Research Methods Section. Retrieved March 8, 2024, from: http://www.asasrms.org/Proceedings/y2015/files/234115.pdf

Download references

Acknowledgements

Authors would like to acknowledge the work of employees of Hungarian Central Statistical Office, who designed and supervised the sampling and the data collection.

Project no. 2020 − 1.1.6-JÖVŐ-2021-00013 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the 2020 − 1.1.6-JÖVŐ funding scheme. The funding source was not involved in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Open access funding provided by Semmelweis University.

Author information

Authors and affiliations.

Center for Health Technology Assessment, Semmelweis University, Üllői út 25, Budapest, 1091, Hungary

András Inotai, Dávid Nagy, Zoltán Kaló & Zoltán Vokó

Syreon Research Institute, Mexikói út 65, Budapest, 1142, Hungary

Dávid Nagy, Zoltán Kaló & Zoltán Vokó

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: ZV, AI. Funding acquisition: ZV. Formal analyses, methodology: ZV, DN. Visualization: AI. Validation: ZV, DN. Project administration: AI. Investigation: ZK, ZV, AI. Writing, original draft: AI. Writing, review and editing: ZK, ZV and DN. All authors read and approved the final manuscript.

Corresponding author

Correspondence to András Inotai .

Ethics declarations

Ethics approval and consent to participate.

The study protocol was approved by the Medical Research Council – Scientific and Ethical Committee in Hungary (number of ethical approval: IV/2292-1 2022/EKU), and the research was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki. Written consent was obtained from each participant.

Consent for publication

Not applicable as only aggregated health data has been published.

Competing interests

Authors state that they have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Inotai, A., Nagy, D., Kaló, Z. et al. Population-level norm values by EQ-5D-3L in Hungary - a comparison of survey results from 2022 with those from 2000. Qual Life Res (2024). https://doi.org/10.1007/s11136-024-03699-9

Download citation

Accepted : 24 May 2024

Published : 05 June 2024

DOI : https://doi.org/10.1007/s11136-024-03699-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Health-related quality of life
• Population survey
• Find a journal
• Publish with us
• Track your research

Help | Advanced Search

Computer Science > Computer Vision and Pattern Recognition

Title: a comprehensive survey on underwater image enhancement based on deep learning.

Abstract: Underwater image enhancement (UIE) is a challenging research task in the field of computer vision. Although hundreds of UIE algorithms have been proposed, a comprehensive and systematic review is still lacking. To promote future research, we summarize the UIE task from multiple perspectives. First, the physical models, data construction processes, evaluation metrics, and loss functions are introduced. Second, according to the contributions brought by different literatures, recent proposed algorithms are discussed and classified from six perspectives, namely network architecture, learning strategy, learning stage, assistance task, domain perspective and disentanglement fusion, respectively. Third, considering the inconsistencies in experimental settings in different literatures, a comprehensive and fair comparison does not yet exist. To this end, we quantitatively and qualitatively evaluate state-of-the-art algorithms on multiple benchmark datasets. Finally, issues worthy of further research in the UIE task are raised. A collection of useful materials is available at this https URL .

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

Explore a career with us

Related articles.

Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale

A generative AI reset: Rewiring to turn potential into value in 2024

Implementing generative AI with speed and safety